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@ARTICLE{BadriSOL03,
  author = {S. Abolmaali and C. Ostermann and A. Zell},
  title = {The Compressed Feature Matrix - a Novel Descriptor for Adaptive Similarity
	Search},
  journal = {J. Mol. Model.},
  year = {2003},
  volume = {9},
  pages = {66 - 75}
}

@ARTICLE{adryan04,
  author = {B. Adryan and R. Schuh},
  title = {{G}ene {O}ntology-based clustering of gene expression data},
  journal = {To appear in {B}ioinformatics},
  year = {2004}
}

@ARTICLE{Agrafiotis2003MolSim,
  author = {D. Agrafiotis and H. Xu},
  title = {{A Geodisc Framework for Analyzing Molecular Similarities}},
  journal = {J. Chem. Inf. Comp. Sci.},
  year = {2003},
  volume = {43},
  pages = {475 - 484},
  owner = {holger},
  timestamp = {2006.10.15}
}

@ARTICLE{AhoTransRed1972,
  author = {A. Aho and M. Garey and J. Ullman},
  title = {The Transitive Reduction of a Directed Graph},
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  pages = {131 - 137},
  number = {2},
  owner = {froehlih},
  timestamp = {2007.04.16}
}

@ARTICLE{AizKernelTrick64,
  author = {M. Aizerman and E. Braverman and L. Rozonoer},
  title = {Theoretical foundations of the potential function method in pattern
	recognition learning},
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  year = {1964},
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  pages = {821 - 837}
}

@CONFERENCE{akahoKCCA01,
  author = {S. Akaho},
  title = {A kernel method for canonical correlation analysis},
  booktitle = {Proc. Int. Meeting Psychometric Society},
  year = {2001}
}

@ARTICLE{Alexa2006topGO,
  author = {Adrian Alexa and J\"org Rahnenführer and Thomas Lengauer},
  title = {{Improved scoring of functional groups from gene expression data
	by decorrelating GO graph structure}},
  journal = {Bioinformatics},
  year = {2006},
  volume = {22},
  pages = {1600 - 1607},
  number = {13},
  owner = {froehlih},
  timestamp = {2007.12.04}
}

@ARTICLE{Lymphoma2000,
  author = {ASH A. ALIZADEH and MICHAEL B. EISEN and R. ERIC DAVIS and CHI M
	and, IZIDORE S. LOSSOS and ANDREAS ROSENWALD and
	
	 JENNIFER C. BOLDRICK and HAJEER SABET and TRUC TRAN and XIN YU and
	JOHN I. POWELL and LIMING YANG and GERALD E. MARTI and 
	
	 TROY MOORE and JAMES HUDSON JR and LISHENG LU and DAVID B. LEWIS
	and ROBERT TIBSHIRANI and GAVIN SHERLOCK and WING C. CHAN and
	
	 TIMOTHY C. GREINER and DENNIS D. WEISENBURGER and JAMES O. ARMITAGE
	and ROGER WARNKE and RONALD LEVY and 
	
	 WYNDHAM WILSON and MICHAEL R. GREVER and JOHN C. BYRD and DAVID BOTSTEIN
	and PATRICK O. BROWN and LOUIS M. STAUDT},
  title = {Distinct types of diffuse large B-cell lymphoma identified by gene
	expression profiling},
  journal = {Nature},
  year = {2000},
  volume = {403},
  pages = {503 - 511}
}

@ARTICLE{AloBar99,
  author = {U. Alon and N. Barkai and D. Notterman and K. Gish and S. Ybarra
	and D. Mack and A. Levine},
  title = {Broad patterns of gene expression revealed by clustering analysis
	of tumor and normal colon cancer tissues probed by oligonucleotide
	arrays},
  journal = {Cell Biology},
  year = {1999},
  volume = {96},
  pages = {6745 - 6750}
}

@ARTICLE{AroRKHS50,
  author = {N. Aronszajn},
  title = {Theory of reproducing kernels},
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  year = {1950},
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}

@INCOLLECTION{Waterbeemd03Book,
  author = {P. Artursson and C. Bergstr\"om},
  title = {Intestinal Absorption: The Role of Polar Surface Area},
  booktitle = {Drug Bioavailability},
  publisher = {Wiley-VCH},
  year = {2003},
  editor = {H. van de Waterbeemd and H. Lennern\"as and P. Artursson},
  pages = {341 - 357},
  address = {Weinheim}
}

@BOOK{BoeSchnBook00,
  title = {Virtual Screening for Bioactive Molecules},
  publisher = {Wiley-VCH},
  year = {2000},
  author = {H.-J. B\"ohm and G. Schneider},
  address = {Weinheim}
}

@ARTICLE{BoehmKlebeHydro02,
  author = {M. B\"ohm and G. Klebe},
  title = {Development of a New Hydrogen-Bond Descriptor and Their Application
	to Comparative Mean Field Analysis},
  journal = {J. Med. Chem.},
  year = {2002},
  volume = {45},
  pages = {1585 - 1597}
}

@ARTICLE{KlebeDescs02,
  author = {M. B\"ohm and G. Klebe},
  title = {{D}evelopment of {N}ew {H}ydrogen--{B}ond {D}escriptors and {T}heir
	{A}pplication to {C}omparative {M}olecular {F}ield {A}nalyses},
  journal = {J. Med. Chem.},
  year = {2002},
  volume = {45},
  pages = {1585--1597},
  abstract = {Knowledge-based descriptors extracted from composite crystal-field
	environments in crystal data have been developed for the description
	of interaction properties of small molecules. Using SuperStar seven
	diverse probe atoms have been selected to reflect the most important
	physicochemical properties. The general application of these descriptors
	in comparative molecular field analysis has been investigated using
	a dataset of thermolysin inhibitors, and a comparison to the GRID
	program has been performed. We especially focused on hydrogen-bond
	donor and acceptor properties by selecting a carbonyl and amino group
	as suitable probes. Their performance has been compared to that of
	the hydrogen-bond descriptors presently implemented in CoMSIA (comparative
	molecular similarity indices analysis). The newly developed descriptors
	produced significantly improved statistics for the correlation analyses
	if they are exclusively applied or, even better, applied in c ombination
	with other CoMSIA descriptors. Two methodologically different approaches
	have been tested to approximate the developed descriptors. Both reduce
	significantly the required computational efforts in particular for
	large data sets. The graphical interpretation of the field contributions
	of hydrogen-bonding properties elucidates additional features compared
	to those obtained from the original CoMSIA method. They are of valuable
	support for the design of improved inhibitors.},
  groupsearch = {0}
}

@ARTICLE{bachKCCAandKICA02,
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  title = {Kernel independent component analysis},
  journal = {J. Machine Learning Research},
  year = {2002},
  volume = {3},
  pages = {1 - 48}
}

@ARTICLE{BalonHIA99,
  author = {K. Balon and B. Riebesehl and B. M\"uller},
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	Passive Intestinal Absorption},
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  year = {1999},
  volume = {16},
  pages = {882 - 888}
}

@ARTICLE{BarthHerz2006,
  author = {Andreas S. Barth and and Ruprecht Kuner and Andreas Buness and Markus
	Ruschhaupt and Sylvia Merk and Ludwig Zwermann and Stefan K\"a\"ab
	and Eckart Kreuzer and Gerhard Steinbeck and Ulrich Mansmann and
	Annemarie Poustka and Michael Nabauer and Holger S\"ultmann},
  title = {Identification of a Common Gene Expression Signature in Dilated Cardiomyopathy
	Across Independent Microarray Studies},
  journal = {Journal of the American College of Cardiology},
  year = {2006},
  volume = {48},
  number = {8},
  owner = {froehlih},
  timestamp = {2006.10.30}
}

@INCOLLECTION{BartlettTaylorSVMBound99,
  author = {P. Bartlett and J. Shawe-Taylor},
  title = {Generalization performance of support vector machines and other pattern
	classifiers},
  booktitle = {Advances in Kernel Methods - Support Vector Learning},
  publisher = {MIT Press},
  year = {1999},
  editor = {B. Sch\"olkopf and C. Burges and A. Smola},
  pages = {43 - 54},
  address = {Cambridge, MA}
}

@ARTICLE{BattitiMIFS94,
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	Net Learning},
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  volume = {5},
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  number = {4}
}

@ARTICLE{BaumWelch70HMMAlgo,
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	probabilistic functions of Markov chains}},
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  year = {1970},
  volume = {41},
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  number = {1}
}

@ARTICLE{beissbarth04,
  author = {T. Beissbarth and T. Speed},
  title = {{GO}stat: finding statistically overexpressed {G}ene {O}ntologies
	within groups of genes},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {1464-1465},
  number = {9},
  doi = {10.1093/bioinformatics/bth088}
}

@ARTICLE{Belisle1992SimAnn,
  author = {Belisle, C. J. P.},
  title = {Convergence theorems for a class of simulated annealing algorithms},
  journal = {J. Applied Probability},
  year = {1992},
  volume = {29},
  pages = {885 - 895},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@TECHREPORT{BelkinSemisup04,
  author = {M. Belkin and P. Niyogi and V. Sindhwani},
  title = {Manifold Regularization: A Geometric Framework for Learning from
	Examples},
  institution = {Dept. of Computer Science, University of Chicago},
  year = {2004},
  number = {TR-2003-06}
}

@ARTICLE{Belozertseva2006FST,
  author = {Belozertseva, I.V. and Kos, T. and Popik, P. and Danysz, W. and Bespalov,
	A.Y.},
  title = {Antidepressant-like effects of mGluR1 and mGlu5 antagonists in the
	rat forced swim and the mouse tail suspension test},
  journal = {Eur. Neuropsychopharmacol.},
  year = {2006},
  note = {Epub ahead of print.},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@ARTICLE{BenHur05ProtProtInteraction,
  author = {A. Ben-Hur and W. Noble},
  title = {{Kernel Methods for Predicting Protein-Protein Interactions}},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {i38 - i46},
  number = {1}
}

@ARTICLE{BenderMolSim04,
  author = {A. Bender and R. Glen},
  title = {Molecular similarity: a key technique in molecular informatics},
  journal = {Org. Biomol. Chem.},
  year = {2004},
  volume = {2},
  pages = {3204 - 3218}
}

@ARTICLE{Benjamini1995FDRControl,
  author = {Benjamini, Y. and Hochberg, Y.},
  title = {{Controlling the False Discovery Rate: a Practical and Powerful Approach
	to Multiple Testing}},
  journal = {J. Royal Statist. Soc., Series B},
  year = {1995},
  volume = {57},
  pages = {289 - 300},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{Benjamini2001BY,
  author = {{Benjamini, Y. and Yekutieli, D.}},
  title = {{The control of the false discovery rate in multiple testing under
	dependency}},
  journal = {Annals of Statistics},
  year = {2001},
  volume = {29},
  pages = {1165 - 1188},
  owner = {froehlih},
  timestamp = {2008.04.08}
}

@BOOK{Berg2004MCMC,
  title = {Markov Chain Monte Carlo Simulations and Their Statistical Analysis},
  publisher = {World Scientific},
  year = {2004},
  author = {B. Berg},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{beyer95,
  author = {Beyer, H.G.},
  title = {Toward a Theory of Evolution Strategies: On the Benefits of Sex -
	the $\mu/\mu,\lambda$ Theory},
  journal = {Evolutionary Computation},
  year = {1995},
  volume = {1},
  pages = {81-111},
  groupsearch = {0},
  keywords = {evolutionary algorithms}
}

@BOOK{BishopNN95,
  title = {Neural Networks for Pattern Recognition},
  publisher = {Clarendon Press},
  year = {1995},
  author = {C. Bishop},
  address = {Oxford}
}

@ARTICLE{BluLan97,
  author = {A. L. Blum and P. Langley},
  title = {{Selection of Relevant Features and Examples in Machine Learning}},
  journal = {Artificial Intelligence},
  year = {1997},
  volume = {97},
  pages = {245 - 271},
  number = {12}
}

@ARTICLE{Bolstad2003QuantNorm,
  author = {Bolstad, B. M. and Irizarry R. A. and Astrand, M. and Speed, T. P.},
  title = {A comparison of normalization methods for high density oligonucleotide
	array data based on bias and variance.},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  pages = {185-193},
  owner = {froehlih},
  timestamp = {2006.11.30}
}

@CONFERENCE{BonWei94,
  author = {B. Bonnlander and A. Weigend},
  title = {Selecting input variables using mutual information and nonparametric
	density estimation},
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  year = {1994},
  pages = {42 - 50}
}

@ARTICLE{Borsini1995FST,
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  journal = {Neurosci. Biobehav. Rev.},
  year = {1995},
  volume = {19},
  pages = {37-46},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@ARTICLE{Borsini1988FST,
  author = {Borsini, F. and Meli, A.},
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	activity?},
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  year = {1988},
  volume = {94},
  pages = {147-160},
  number = {2},
  owner = {froehlih},
  timestamp = {2006.07.14}
}

@CONFERENCE{BoserSVM92,
  author = {B. Boser and M. Guyon and V. Vapnik},
  title = {A training algorithm for optimal margin classifiers},
  booktitle = {Proc. 5th Ann. ACM Workshop on Comp. Learning Theory},
  year = {1992},
  editor = {D. Haussler},
  address = {Pittsburgh, PA},
  publisher = {ACM Press}
}

@ARTICLE{Boutros2002Data,
  author = {M. Boutros and H. Agaisse and N. Perrimon},
  title = {Sequential activation of signaling pathways during innate immune
	responses in \emph{Drosophila}},
  journal = {Developmental Cell},
  year = {2002},
  volume = {3},
  pages = {711 - 722},
  number = {5},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{BradAUC97,
  author = {P. Bradley},
  title = {{The use of the are under the ROC curve in the evaluation of machine
	learning algorithms}},
  journal = {Pattern Recognition},
  year = {1997},
  volume = {30},
  pages = {1145 - 1159}
}

@CONFERENCE{BradleyFeatSelSVM98,
  author = {P. Bradley and O. Mangasarian},
  title = {Feature Selection via Concave Minimization and Support Vector Machines},
  booktitle = {Proc. 13th Int. Conf. Machine Learning},
  year = {1998},
  pages = {82 - 90}
}

@ARTICLE{BreimanRandomForests2001,
  author = {L. Breiman},
  title = {Random Forests},
  journal = {Machine Learning},
  year = {2001},
  volume = {45},
  pages = {5 - 32},
  number = {1},
  owner = {froehlih},
  timestamp = {2006.10.26}
}

@BOOK{BreimanCART84,
  title = {Classification and Regression Trees},
  publisher = {Wadsworth and Brooks},
  year = {1984},
  author = {L. Breiman and J. Friedman and R. Olshen and C. Stone}
}

@INCOLLECTION{budanitsky04,
  author = {A. Budanitsky and G. Hirst},
  title = {Semantic distance in {W}ord{N}et: {A}n experimental, application-oriented
	evaluation of five measures.},
  booktitle = {Workshop on {W}ord{N}et and other Lexical Resources, Second meeting
	of the {N}ord {A}merican Chapter of the Association for Computational
	Linguistics},
  year = {2001},
  address = {Pittsburgh}
}

@ARTICLE{Schneider03,
  author = {E. Byvatov and U. Fechner and J. Sadowski and G. Schneider},
  title = {{Comparison of Support Vector Machine and Artificial Neural Network
	Systems for Drug/Nondrug Classification}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {43},
  pages = {1882 - 1889},
  number = {6}
}

@ARTICLE{ByvatovSVMDESCSEL04,
  author = {E. Byvatov and G. Schneider},
  title = {SVM-Based Feature Selection for Characterization of Focused Compound
	Collections},
  journal = {J. Chem. Inf. Comp. Sci.},
  year = {2004},
  volume = {44},
  pages = {993 - 999},
  number = {3}
}

@ARTICLE{CarhartAtomPair85,
  author = {R. Carhart and D. Smith and R. Venkataraghavan},
  title = {Atom Pairs as Molecular Features in Structure-Activity Studies: Definitionand
	Applications},
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  year = {1985},
  volume = {25},
  pages = {64 - 73}
}

@MANUAL{libsvm01,
  title = {{LIBSVM: a library for support vector machines}},
  author = {C. Chang and C. Lin},
  year = {2001},
  note = {Available from http://www.csie.ntu.edu.tw/$\sim$cjlin/libsvm}
}

@CONFERENCE{ChaVap00,
  author = {O. Chapelle and V. Vapnik},
  title = {{Model selection for Support Vector Machines}},
  booktitle = {Adv. Neural Inf. Proc. Syst. 12},
  year = {2000},
  editor = {S. Solla and T. Leen and K.-R. M\"uller},
  address = {Cambridge, MA},
  publisher = {MIT Press}
}

@ARTICLE{ChaVapBouMuk98,
  author = {O. Chapelle and V. Vapnik and O. Bousqet and S. Mukherjee},
  title = {{Choosing Multiple Parameters for Support Vector Machines}},
  journal = {Machine Learning},
  year = {2002},
  volume = {46},
  pages = {131 - 159},
  number = {1}
}

@CONFERENCE{ChapClustKer03,
  author = {O. Chapelle and J. Weston and B. Sch\"okopf},
  title = {Cluster Kernels for Semi-Supervised Learning},
  booktitle = {Neural Inf. Proc. Syst.},
  year = {2003}
}

@CONFERENCE{ChapSemiSup05,
  author = {O. Chapelle and A. Zien},
  title = {Semi-Supervised Classification by Low Density Separation},
  booktitle = {AI \& Statistics},
  year = {2005}
}

@CONFERENCE{Chen99GRNNPCompleteness,
  author = {T. Chen and V. Filkov and S. Skiena},
  title = {Identifying gene regulatory networks from experimental data},
  booktitle = {Proc. 3rd Ann. Int. Conf. on Comp. Mol. Biology},
  year = {1999},
  publisher = {ACM-SIGACT}
}

@ARTICLE{ChenPharmacophoreIdent99,
  author = {X. Chen and A. Rusinko and A. Tropsha and S. S. Young},
  title = {{A}utomated {P}harmacophore {I}dentification for {L}arge {C}hemical
	{D}ata {S}ets},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {1999},
  volume = {39},
  pages = {887--896}
}

@ARTICLE{cherkasskySVRModSel,
  author = {V. Cherkassky and Y. Ma},
  title = {Practical Selection of SVM Parameters and Noise Estimation for SVM
	Regression},
  journal = {Neural Networks},
  year = {2004},
  volume = {17},
  pages = {113 - 126},
  number = {1}
}

@ARTICLE{Chewawiwat1999,
  author = {N. Chewawiwat and M. Yano and K. Terada and N. J. Hoogenraad and
	M. Mori},
  title = {Characterization of the novel mitochondrial protein import component,
	Tom34, in mammalian cells.},
  journal = {J Biochem},
  year = {1999},
  volume = {125},
  pages = {721--727},
  number = {4},
  month = {Apr},
  abstract = {Tom34 is a newly-found component of the mitochondrial protein import
	machinery in mammalian cells with no apparent counterpart in fungi.
	RNA blot and immunoblot analyses showed that the expression of Tom34
	varies among tissues and differs from that of the core translocase
	component Tom20. In contrast to a previous report [Nuttal, S.D. et
	al. (1997) DNA Cell Biol. 16, 1067-1074], the present study using
	a newly-prepared anti-Tom34 antibody with a high titer showed that
	Tom34 is present largely in the cytosolic fraction and partly in
	the mitochondrial and membrane fractions after fractionation of tissues
	and cells, and that the membrane-associated form is largely extractable
	with 0.1 M sodium carbonate. The in vitro import of preproteins into
	isolated rat mitochondria was strongly inhibited by DeltahTom34 which
	lacks the NH2-terminal hydrophobic region of human Tom34 (hTom34).
	Import was also strongly inhibited by anti-hTom34. In pulse-chase
	experiments using COS-7 cells, pre-ornithine transcarbamylase (pOTC)
	was rapidly processed to the mature form. Coexpression of hTom34
	resulted in a stimulation of pOTC processing, whereas the coexpression
	of hTom34 antisense RNA caused inhibition. The results confirm that
	Tom34 plays a role in mitochondrial protein import in mammals, and
	suggest it to be an ancillary component of the translocation machinery
	in mammalian cells.},
  keywords = {Animals; Base Sequence; COS Cells; Carrier Proteins; Cytosol; DNA
	Primers; Enzyme Precursors; Female; Hela Cells; Humans; Male; Membrane
	Proteins; Membrane Transport Proteins; Membranes; Mitochondria; Mitochondrial
	Membrane Transport Proteins; Ornithine Carbamoyltransferase; Pregnancy;
	Protein Processing, Post-Translational; RNA, Messenger; Rats; Receptors,
	Cell Surface; Recombinant Proteins; Swine; Tissue Distribution},
  owner = {froehlih},
  pmid = {10101285},
  timestamp = {2008.04.02}
}

@ARTICLE{cho01,
  author = {R. Cho and M. Huang and M. Campbell and H. Dong and L. Steinmetz
	and L. Sapinoso and G. Hampton and S. Elledge and R. Davis and D.
	Lockhart},
  title = {Transcriptional regulation and function during the human cell cycle},
  journal = {Nature Genetics},
  year = {2001},
  volume = {27},
  pages = {48-54},
  number = {1},
  groupsearch = {0},
  keywords = {biological datasets}
}

@ARTICLE{cho98,
  author = {Cho, R.J. and Campbell M.J. and Winzeler, E.A. and Steinmetz, L.
	and Conway, A. and Wodicka, L. and Wolfsberg, T.G. and Gabrielian,
	A.E. and Landsman, D. and Lockhart, D.J. and Davis, R.W.},
  title = {A Genome wide transcriptional analysis of the mititic cell cycle},
  journal = {Molecular Cell},
  year = {1998},
  volume = {2},
  pages = {65-73},
  groupsearch = {0},
  keywords = {original papers on biological datasets}
}

@ARTICLE{ChowLiuMWST68,
  author = {C. Chow and C. Liu},
  title = {Approximating discrete probability distribitions with dependence
	trees},
  journal = {IEEE Trans. Inf. Theory},
  year = {1968},
  volume = {14},
  pages = {462 - 467},
  number = {3}
}

@ARTICLE{chu98,
  author = {Chu, S. and DeRisi J. and Eisen, M. and Mullholland, J. and Botstein,
	D. and Brown, P.O. and Herskowitz, I.},
  title = {The transcriptional program of sporulation in budding yeast},
  journal = {Science},
  year = {1998},
  volume = {282},
  pages = {699-705},
  groupsearch = {0},
  keywords = {original papers on biological datasets}
}

@BOOK{ChungSpectralGraph97,
  title = {Spectral Graph Theory},
  publisher = {American Mathematical Society},
  year = {1997},
  author = {F. Chung},
  number = {92},
  series = {CBMS Regional Conference Series in Mathematics},
  address = {Providence, RI}
}

@ARTICLE{ParamselSvm03,
  author = {K. Chung and W. Kao and C. Sun and L. Wang and C. Lin},
  title = {Radius Margin Bounds for Support Vector Machines with the RBF Kernel},
  journal = {Neural Computation},
  year = {2003},
  volume = {15},
  pages = {2643 - 2681},
  number = {11}
}

@INPROCEEDINGS{Cohn95ActiveLearning,
  author = {David A. Cohn and Zoubin Ghahramani and Michael I. Jordan},
  title = {Active Learning with Statistical Models},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {1995},
  editor = {G. Tesauro and D. Touretzky and T. Leen},
  volume = {7},
  pages = {705--712},
  publisher = {The {MIT} Press},
  url = {citeseer.ist.psu.edu/article/cohn96active.html}
}

@ARTICLE{coller00,
  author = {Coller, H.A. and Grandori, C. and Tamayo, P. and Colbert, T. and
	Lande, E.S. and Eisenman, R.N. and Golub T.R.},
  title = {Expression analysis with oligonucleotide microarrays reveals that
	MYC regulates genes involved in growth, cell cycle, signaling, and
	adhesion},
  journal = {PNAS},
  year = {2000},
  volume = {97},
  pages = {3260-3265},
  groupsearch = {0},
  keywords = {supervised methods}
}

@ARTICLE{Consortium2004NumOfGenes,
  author = {International Human Genome Sequencing Consortium},
  title = {Finishing the euchromatic sequence of the human genome.},
  journal = {Nature},
  year = {2004},
  volume = {431},
  pages = {931--945},
  number = {7011},
  month = {Oct},
  abstract = {The sequence of the human genome encodes the genetic instructions
	for human physiology, as well as rich information about human evolution.
	In 2001, the International Human Genome Sequencing Consortium reported
	a draft sequence of the euchromatic portion of the human genome.
	Since then, the international collaboration has worked to convert
	this draft into a genome sequence with high accuracy and nearly complete
	coverage. Here, we report the result of this finishing process. The
	current genome sequence (Build 35) contains 2.85 billion nucleotides
	interrupted by only 341 gaps. It covers approximately 99\% of the
	euchromatic genome and is accurate to an error rate of approximately
	1 event per 100,000 bases. Many of the remaining euchromatic gaps
	are associated with segmental duplications and will require focused
	work with new methods. The near-complete sequence, the first for
	a vertebrate, greatly improves the precision of biological analyses
	of the human genome including studies of gene number, birth and death.
	Notably, the human genome seems to encode only 20,000-25,000 protein-coding
	genes. The genome sequence reported here should serve as a firm foundation
	for biomedical research in the decades ahead.},
  keywords = {Amino Acid Sequence; Base Sequence; Centromere; Chromosomes, Artificial,
	Bacterial; Chromosomes, Human; DNA, Complementary; Euchromatin; Gene
	Duplication; Genes; Genome, Human; Heterochromatin; Human Genome
	Project; Humans; Molecular Sequence Data; Multigene Family; Physical
	Chromosome Mapping; Plasmids; Pseudogenes; Research Design; Sensitivity
	and Specificity; Sequence Analysis, DNA; Telomere},
  owner = {froehlih},
  pmid = {15496913},
  timestamp = {2008.04.22}
}

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  address = {Cambridge, Massachusetts},
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}

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	2003) },
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@CONFERENCE{Couto2005GraSM,
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	Selecting Disjunctive Ancestors}},
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  year = {2005},
  owner = {froehlih},
  timestamp = {2006.08.17}
}

@TECHREPORT{Couto2003FuSSiMeg,
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  title = {{Implementation of a Functional Semantic Similarity Measure between
	Gene-Products}},
  institution = {Department of Informatics, University of Lisbon},
  year = {2003},
  number = {DI/FCUL TR 03--29},
  owner = {froehlih},
  timestamp = {2006.08.17}
}

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@BOOK{CrisSha00,
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  publisher = {Cambridge University Press},
  year = {2000},
  author = {N. Cristianini and J. Shawe-Taylor}
}

@CONFERENCE{Cris02,
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  title = {On kernel-target alignment},
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}

@ARTICLE{Cryan2002FST,
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	and future needs},
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  year = {2002},
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}

@ARTICLE{CsatoSparseGP02,
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}

@ARTICLE{davies79,
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@CONFERENCE{DennisPatternSearch94,
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	and Optimization},
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@ARTICLE{Detke1995FST,
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	by serotonergic and noradrenergic antidepressants},
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  owner = {froehlih},
  timestamp = {2007.08.22}
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@CONFERENCE{DingPCAKmeans,
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  booktitle = {Proc. Int. Conf. on Machine Learning},
  year = {2004},
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@CONFERENCE{DingMinMaxCut01,
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  title = {Spectral Min-Max Cut for Graph Partitioning and Data Clustering},
  booktitle = {Proc. 1st IEEE Int. Conf. Data Mining},
  year = {2001},
  pages = {107 -114}
}

@ARTICLE{doniger03,
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	S.C. Lawlor and B.R. Conklin},
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  journal = {Genome Biology},
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@ARTICLE{Driessche2005Epistasis,
  author = {N. Van Driessche and J. Demsar and E. Booth and P. Hill and P. Juvan
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  journal = {Nature Genetics},
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  pages = {471 - 477},
  number = {5},
  owner = {froehlih},
  timestamp = {2007.09.17}
}

@BOOK{DudaHart2001,
  title = {Pattern Classification},
  publisher = {Wiley-Interscience},
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  author = {R. Duda and P. Hart and D. Stork},
  address = {New York}
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@ARTICLE{Efron2002localFDR,
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  journal = {Genetic Epidemiology},
  year = {2002},
  volume = {23},
  pages = {70 - 86},
  owner = {froehlih},
  timestamp = {2007.02.02}
}

@INPROCEEDINGS{eisen98,
  author = {M. Eisen and P. Spellman and D. Botstein and P. Brown},
  title = {Cluster Analysis and Display of Genome-wide Expression Patterns.},
  booktitle = {Proceedings of the National Academy of Sciences, USA},
  year = {1998},
  volume = {95},
  pages = {14863-14867},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@BOOK{Eshelman91,
  title = {The CHC Adaptive Search Algorithm, How to Have a Safe Search When
	Engaging in Non-traditional Genetic Recombination},
  publisher = {Morgan Kaufman},
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}

@ARTICLE{feherBBB00,
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  journal = {Int. J. Pharmaceut.},
  year = {2000},
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}

@BOOK{fellbaum98,
  title = {{WordNet. An electronic lexical database}},
  publisher = {MIT Press},
  year = {1998},
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@ARTICLE{FigueareasRingPercept96,
  author = {J. Figueras},
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	than depth-first ring detection, a result expected on the basis of
	recent work that establishes polynomial order for BFS.2},
  contents = {Smallest Set of Smallest Ring (SSSR), Bread First Search (BFS), Binary
	Edge Encoded Path (BEEP), message passing algorithm}
}

@ARTICLE{FireMelloRNAinterference1998,
  author = {Fire, A. and Xu, S. and Montgomery, M.K. and Kostas, S.A. and Driver,
	S.E. and Mello, C.C.},
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	Caenorhabditis elegans.},
  journal = {Nature},
  year = {1998},
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}

@ARTICLE{Flicek2008Ensembl,
  author = {P. Flicek and B. L. Aken and K. Beal and B. Ballester and M. Caccamo
	and Y. Chen and L. Clarke and G. Coates and F. Cunningham and T.
	Cutts and T. Down and S. C. Dyer and T. Eyre and S. Fitzgerald and
	J. Fernandez-Banet and S. Gräf and S. Haider and M. Hammond and R.
	Holland and K. L. Howe and K. Howe and N. Johnson and A. Jenkinson
	and A. Kähäri and D. Keefe and F. Kokocinski and E. Kulesha and D.
	Lawson and I. Longden and K. Megy and P. Meidl and B. Overduin and
	A. Parker and B. Pritchard and A. Prlic and S. Rice and D. Rios and
	M. Schuster and I. Sealy and G. Slater and D. Smedley and G. Spudich
	and S. Trevanion and A. J. Vilella and J. Vogel and S. White and
	M. Wood and E. Birney and T. Cox and V. Curwen and R. Durbin and
	X. M. Fernandez-Suarez and J. Herrero and T. J P Hubbard and A. Kasprzyk
	and G. Proctor and J. Smith and A. Ureta-Vidal and S. Searle},
  title = {Ensembl 2008},
  journal = {Nucleic Acids Res},
  year = {2008},
  volume = {36},
  pages = {D707--D714},
  number = {Database issue},
  month = {Jan},
  doi = {10.1093/nar/gkm988},
  keywords = {Animals; Computer Graphics; Databases, Genetic; Genomics; Humans;
	Internet; Mice; Regulatory Elements, Transcriptional; Software; User-Computer
	Interface},
  owner = {froehlih},
  pii = {gkm988},
  pmid = {18000006},
  timestamp = {2008.04.01},
  url = {http://dx.doi.org/10.1093/nar/gkm988}
}

@MASTERSTHESIS{Froe02,
  author = {H. Fr\"ohlich},
  title = {{Feature Selection for Support Vector Machines by Means of Genetic
	Algorithms}},
  school = {University of Marburg},
  year = {2002},
  note = {http://www-ra/informatik.uni-tuebingen.de/mitarb/froehlich}
}

@ARTICLE{FroeChapScho04FeatselGA,
  author = {H. Fr\"ohlich and O. Chapelle and B. Sch\"olkopf},
  title = {{Feature Selection for Support Vector Machines using Genetic Algorithms}},
  journal = {Int. J. AI Tools: Special Issue on Selected Papers from the 15th
	IEEE Int. Conf. on Tools with AI 2003},
  year = {2004},
  volume = {13},
  pages = {791 - 800},
  number = {4}
}

@CONFERENCE{Froe03,
  author = {H. Fr\"ohlich and O. Chapelle and B. Sch\"olkopf},
  title = {{Feature Selection for Support Vector Machines by Means of Genetic
	Algorithms}},
  booktitle = {Proc. 15th IEEE Int. Conf. on Tools with AI},
  year = {2003},
  pages = {142 - 148}
}

@ARTICLE{Frohlich2008NEMsBioinformatics,
  author = {H. Fr\"ohlich and M. Fellmann and H. S\"ultmann and A. Poustka and
	T. Bei{\ss}barth},
  title = {Estimating Large Scale Signaling Networks through Nested Effect Models
	with Intervention Effects from Microarray Data},
  journal = {Bioinformatics},
  year = {2008},
  volume = {1},
  note = {doi: 10.1093/bioinformatics/btm634},
  owner = {froehlih},
  timestamp = {2008.01.10}
}

@CONFERENCE{Frohlich2007GCBRNAi,
  author = {H. Fr\"ohlich and M. Fellmann and H. S\"ultmann and A. Poustka and
	T. Bei{\ss}barth},
  title = {Estimating Large Scale Signaling Networks through Nested Effects
	Models from Intervention Effects in Microarray Data},
  booktitle = {Proc. German Conf. on Bioinformatics},
  year = {2007},
  pages = {45 - 54},
  owner = {froehlih},
  timestamp = {2007.09.14}
}

@ARTICLE{Frohlich2007RNAiBMC,
  author = {H. Fr\"ohlich and M. Fellmann and H. S\"ultmann and A. Poustka and
	T. Bei{\ss}barth},
  title = {Large Scale Statistical Inference of Signaling Pathways from RNAi
	and Microarray Data},
  journal = {BMC Bioinformatics},
  year = {2007},
  volume = {8},
  pages = {386},
  owner = {froehlih},
  timestamp = {2007.10.01}
}

@CONFERENCE{FroeSpikes05,
  author = {H. Fr\"ohlich and B. Naundorf and M. Volgushev and F. Wolf},
  title = {Which Features Trigger Action Potentials in Cortical Neurons in Vivo?},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2005},
  pages = {250 - 255}
}

@ARTICLE{Froehlich2007GOSim,
  author = {H. Fr\"ohlich and N. Speer and A. Poustka and T. Beissbarth},
  title = {{GOSim - An R-Package for Computation of Information Theoretic GO
	Similarities Between Terms and Gene Products}},
  journal = {BMC Bioinformatics},
  year = {2007},
  volume = {8},
  pages = {166},
  owner = {froehlih},
  timestamp = {2007.12.07}
}

@CONFERENCE{FroeSpeerGOKer06,
  author = {H. Fr\"ohlich and N. Speer and A. Zell},
  title = {Kernel Based Functional Gene Grouping},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2006},
  pages = {6886 - 6891}
}

@CONFERENCE{FroehOAKernelsICML05,
  author = {H. Fr\"ohlich and J. Wegner and F. Sieker and A. Zell},
  title = {Optimal Assignment Kernels For Attributed Molecular Graphs},
  booktitle = {Proc. Int. Conf. Machine Learning},
  year = {2005},
  editor = {L. De Raedt and S. Wrobel},
  pages = {225 - 232},
  publisher = {ACM Press}
}

@ARTICLE{FroehOAKernelsQSAR05,
  author = {H. Fr\"ohlich and J. Wegner and F. Sieker and A. Zell},
  title = {Kernel Functions for Attributed Molecular Graphs -- A New Similarity
	Based Approach To ADME Prediction in Classification and Regression},
  journal = {QSAR \& Comb. Sci.},
  year = {2006},
  volume = {25},
  pages = {317 - 326},
  number = {4},
  publisher = {Wiley Interscience}
}

@CONFERENCE{FroehOAKernelsIJCNN05,
  author = {H. Fr\"ohlich and J. Wegner and A. Zell},
  title = {Assignment Kernels For Chemical Compounds},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2005},
  pages = {913 - 918}
}

@ARTICLE{FroeQSAR04,
  author = {H. Fr\"ohlich and J. K. Wegner and A. Zell},
  title = {{T}owards {O}ptimal {D}escriptor {S}ubset {S}election with {S}upport
	{V}ector {M}achines in {C}lassification and {R}egression},
  journal = {QSAR \& Comb. Sci.},
  year = {2004},
  volume = {23},
  pages = {311--318},
  abstract = {In this paper we present a novel method for selecting descriptor subsets
	by means of Support Vector Machines in classification and regression
	- the Incremental Regularized Risk Minimization (IRRM) algorithm.
	In contrast to many other wrapper methods it is fully deterministic
	and computationally efficient. We compare our method to existing
	algorithms and present results on a Human Intestinal Absorption (HIA)
	classification data set and the Huuskonen regression data set for
	aqueous solubility.}
}

@CONFERENCE{FroehEPSGO05,
  author = {H. Fr\"ohlich and A. Zell},
  title = {Efficient Parameter Selection for Support Vector Machines in Classification
	and Regression via Model-Based Global Optimization},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2005},
  pages = {1431 - 1438}
}

@CONFERENCE{Froe04,
  author = {H. Fr\"ohlich and A. Zell},
  title = {{Feature Subset Selection for Support Vector Machines by Incremental
	Regularized Risk Minimization}},
  booktitle = {Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN)},
  year = {2004},
  volume = {3},
  pages = {2041 - 2046}
}

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@ARTICLE{GatViks2006FactorGraph,
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  year = {2004},
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  year = {1999},
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  pages = {531 - 537}
}

@ARTICLE{golub99,
  author = {Golub, T.R. and Slonim, D.K. and Tamayo, P. and Huard, C. and Gaasenbeek,
	M. and Mesirov, J.P. and Coller, H. and Loh, M.L. and Downing, J.R.
	and Caliguri, M.A. and Bloomfield, C.D. and Lander, E.S.},
  title = {Molecular Classification of Cancer: Class Discovery by Gene Expression
	Monitoring},
  journal = {Science},
  year = {1999},
  volume = {286},
  pages = {531-537},
  groupsearch = {0},
  keywords = {supervised methods}
}

@ARTICLE{OrlandRGonzalez10112006SimAnn,
  author = {Gonzalez, Orland R. and Kuper, Christoph and Jung, Kirsten and Naval,
	Prospero C., Jr. and Mendoza, Eduardo},
  title = {{Parameter estimation using Simulated Annealing for S-system models
	of biochemical networks}},
  journal = {Bioinformatics},
  year = {2006},
  pages = {btl522},
  abstract = {Motivation: High-throughput technologies now allow the acquisition
	of biological data such as comprehensive biochemical time-courses
	at unprecedented rates. These temporal profiles carry topological
	and kinetic information regarding the biochemical network from which
	they were drawn. Retrieving this information will require systematic
	application of both experimental and computational methods.Results:
	S-systems are non-linear mathematical approximative models based
	on the power-law formalism. They provide a general framework for
	the simulation of integrated biological systems exhibiting complex
	dynamics such as genetic circuits, signal transduction and metabolic
	networks. We describe how the heuristic optimization technique Simulated
	Annealing can be effectively used for estimating the parameters of
	S-systems from time-course biochemical data. We demonstrate our methods
	using 3 artificial networks designed to simulate different network
	topologies and behavior. We then end with an application to a real
	biochemical network by creating a working model for the cadBA system
	in E. coli.Availability: The source code written in C++ is available
	at http://www.engg.upd.edu.ph/~naval/bioinformcode.html. All the
	necessary programs including the required compiler are described
	in a document archived with the source code.},
  doi = {10.1093/bioinformatics/btl522},
  eprint = {http://bioinformatics.oxfordjournals.org/cgi/reprint/btl522v1.pdf},
  url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btl522v1}
}

@ARTICLE{gowda78,
  author = {Gowda, K.C. and Krishna, G.},
  title = {Agglomertative clustering using the concept of mutual nearest neighborhood},
  journal = {Pattern Recognition},
  year = {1978},
  volume = {10},
  pages = {105-112},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@ARTICLE{GutmannRBFOpt01,
  author = {H. Gutmann},
  title = {A radial basis function method for global optimization},
  journal = {J. Global Optimization},
  year = {2001},
  volume = {19},
  pages = {201 - 227},
  number = {3}
}

@ARTICLE{GuyEli03,
  author = {I. Guyon and A. Elisseeff},
  title = {{An Introduction into Variable and Feature Selection}},
  journal = {{J. Machine Learning Research Special Issue on Variable and Feature
	Selection}},
  year = {2003},
  volume = {3},
  pages = {1157 - 1182}
}

@ARTICLE{GuyWesBarVap02,
  author = {I. Guyon and J. Weston and S. Barnhill and V. Vapnik},
  title = {{Gene Selection for Cancer Classification using Support Vector Machines}},
  journal = {Machine Learning},
  year = {2002},
  volume = {46},
  pages = {389 - 422}
}

@CONFERENCE{gaertnerGraphKer03,
  author = {T. G{\"a}rtner and P. Flach and S. Wrobel},
  title = {{On graph kernels: Hardness results and efficient alternatives}},
  booktitle = {Proc. 16th Ann. Conf. Comp. Learning Theory and 7th Ann. Workshop
	on Kernel Machines},
  year = {2003}
}

@ARTICLE{Hahne2008,
  author = {Florian Hahne and Alexander Mehrle and Dorit Arlt and Annemarie Poustka
	and Stefan Wiemann and Tim Beissbarth},
  title = {Extending pathways based on gene lists using InterPro domain signatures.},
  journal = {BMC Bioinformatics},
  year = {2008},
  volume = {9},
  pages = {3},
  abstract = {BACKGROUND: High-throughput technologies like functional screens and
	gene expression analysis produce extended lists of candidate genes.
	Gene-Set Enrichment Analysis is a commonly used and well established
	technique to test for the statistically significant over-representation
	of particular pathways. A shortcoming of this method is however,
	that most genes that are investigated in the experiments have very
	sparse functional or pathway annotation and therefore cannot be the
	target of such an analysis. The approach presented here aims to assign
	lists of genes with limited annotation to previously described functional
	gene collections or pathways. This works by comparing InterPro domain
	signatures of the candidate gene lists with domain signatures of
	gene sets derived from known classifications, e.g. KEGG pathways.
	RESULTS: In order to validate our approach, we designed a simulation
	study. Based on all pathways available in the KEGG database, we create
	test gene lists by randomly selecting pathway genes, removing these
	genes from the known pathways and adding variable amounts of noise
	in the form of genes not annotated to the pathway. We show that we
	can recover pathway memberships based on the simulated gene lists
	with high accuracy. We further demonstrate the applicability of our
	approach on a biological example. CONCLUSION: Results based on simulation
	and data analysis show that domain based pathway enrichment analysis
	is a very sensitive method to test for enrichment of pathways in
	sparsely annotated lists of genes. An R based software package domainsignatures,
	to routinely perform this analysis on the results of high-throughput
	screening, is available via Bioconductor.},
  doi = {10.1186/1471-2105-9-3},
  keywords = {Algorithms; Amino Acid Sequence; Database Management Systems; Databases,
	Protein; Gene Expression Profiling; Molecular Sequence Data; Oligonucleotide
	Array Sequence Analysis; Protein Structure, Tertiary; Proteins; Signal
	Transduction; Structure-Activity Relationship},
  owner = {froehlih},
  pii = {1471-2105-9-3},
  pmid = {18177498},
  timestamp = {2008.03.25},
  url = {http://dx.doi.org/10.1186/1471-2105-9-3}
}

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  author = {T. A. Halgren},
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  pages = {490--641},
  doi = {10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P},
  owner = {JoergKurtWegner},
  timestamp = {2006.10.15}
}

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  author = {D. Hanisch and A. Zien and R. Zimmer and T. Lengauer},
  title = {Co-clustering of biological networks and gene expression data},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18 (Supplement)},
  pages = {S145-S154},
  groupsearch = {0},
  keywords = {co-clustering methods}
}

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  author = {Harel, D. and Koren, Y.},
  title = {Clustering Spatial Data Using Random Walks},
  booktitle = {Proceedings of the seventh ACM SIGKDD international conference on
	Knowledge discovery and data mining},
  year = {2001},
  pages = {281-286},
  publisher = {ACM Press, New York, NY, USA},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@INPROCEEDINGS{hartemink01,
  author = {Hartemink, A.J. and Gifford, D.K. and Jaakkola, T.S. and Young, R.A.},
  title = {Using graphical models and genomic expression data to statistically
	validate models of genetic regulatory networks},
  booktitle = {Proceedings of the 2001 Pacific Symposium on Biocomputing (PSB 2001)},
  year = {2001},
  pages = {422-33.},
  groupsearch = {0},
  keywords = {gene network inference}
}

@ARTICLE{HastieSVMModeSel04,
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  title = {The Entire Regularization Path for Support Vector Machines},
  journal = {J. Machine Learning Research},
  year = {2004},
  volume = {5},
  pages = {1391 - 1415}
}

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  title = {The Elements of Statistical Learning},
  publisher = {Springer},
  year = {2001},
  author = {T. Hastie and R. Tibshirani and J. Friedman},
  owner = {froehlih},
  timestamp = {2006.10.23}
}

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  title = {Convolution Kernels on Discrete Structures},
  institution = {University of California Santa Cruz},
  year = {1999},
  number = {UCSC-CRL-99-10}
}

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  title = {A Tutorial on Learning With Bayesian Networks},
  journal = {Data Mining and Knowledge Discovery},
  year = {1997},
  volume = {1},
  pages = {79 - 119}
}

@ARTICLE{vanHelden00,
  author = {van Helden, J. and Gilbert, D. and Wernisch, L. and Schroeder, K.
	and Wodak, S.},
  title = {Applications of regulatory sequence analysis and metabolic network
	analysis to the interpretation of gene expression data},
  journal = {LNCS},
  year = {2000},
  volume = {2066},
  pages = {155-172},
  groupsearch = {0},
  keywords = {pathway scoring methods}
}

@ARTICLE{Helma01,
  author = {C. Helma and R. King and S. Kramer},
  title = {The Predictive Toxicology Challenge 2000-2001},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {107 - 108}
}

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}

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}

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  title = {Relations between two sets of variables},
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  volume = {28},
  pages = {321 - 377}
}

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  author = {H. Hotelling},
  title = {Analysis of a complex of statistical variables into principal components},
  journal = {J. Educat. Psychol.},
  year = {1933},
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}

@ARTICLE{HouBBB03,
  author = {T. Hou and X. Xu},
  title = {ADME Evaluation in Drug Discovery. 3. Modelling Blood-Brain Barrier
	Partitioning Using Simple Molecular Descriptors},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {43},
  pages = {2137 - 2152},
  number = {6}
}

@ARTICLE{Huber2002VSN,
  author = {W. Huber and A. Heydebreck and H. S\"ultmann and A. Poustka and M.
	Vingron},
  title = {Variance Stabilization Applied to Microarray Data Calibration and
	to the Quantification of Differential Expression},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18},
  pages = {S96 - S104},
  owner = {froehlih},
  timestamp = {2006.11.30}
}

@MISC{fibroblastData,
  author = {{Human Fibroblast Serum Response Dataset}},
  howpublished = {\\http://genome-www.stanford.edu/serum/data.html},
  year = {1999},
  groupsearch = {0},
  keywords = {biological datasets}
}

@ARTICLE{Huuskonen00,
  author = {J. Huuskonen},
  title = {{Estimation of Aqueous Solubility for Diverse Set of Organic Compounds
	Based on Molecular Topology}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2000},
  volume = {40},
  pages = {773-777}
}

@ARTICLE{hvidsten03,
  author = {T. Hvidsten and A. Laegreid and J. Komorowski},
  title = {Learning rule-based models of biological process from gene expression
	time profiles using {G}ene {O}ntology},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  pages = {1116-1123},
  number = {9},
  keywords = {gene ontology supervised methods}
}

@CONFERENCE{Imoto2003NetworkPrior,
  author = {Imoto, S., Higuchi and T., Goto, T. and Tashiro, K. and Kuhara, S.
	and Miyano, S.},
  title = {Combining Microarrays and Biological Knowledge for Estimating Gene
	Networks via Bayesian Networks},
  booktitle = {Proc. 2nd Computational Systems Bioinformatics},
  year = {2003},
  pages = {104 - 113},
  journal = {Proc. 2nd Computational Systems Bioinformatics},
  owner = {froehlih},
  timestamp = {2007.12.14}
}

@CONFERENCE{Inokuchi00,
  author = {A. Inokuchi and T. Washio and H. Motoda},
  title = {An Apriori-based algorithm for mining frequent substructures from
	graph data},
  booktitle = {Proc. 4th PKDD},
  year = {2000},
  pages = {13 - 23}
}

@ARTICLE{iyer99,
  author = {V. Iyer and M. Eisen and D. Ross and G. Schuler and T. Moore and
	J. Lee and J. Trent and L. Staudt and J. Hudson Jr, and M. Boguski
	and D. Lashkari and D. Shalon and D. Botstein and P. Brown},
  title = {The Transcriptional Program in Response of Human Fibroblasts to Serum},
  journal = {Science},
  year = {1999},
  volume = {283},
  pages = {83-87},
  groupsearch = {0},
  keywords = {original papers on biological datasets}
}

@CONFERENCE{JakHau99,
  author = {T. S. Jaakkola and D. Haussler},
  title = {Probalistic kernel regression models},
  booktitle = {Proc. 1999 Conf. AI and Statistics},
  year = {1999}
}

@BOOK{jain88,
  title = {Algorithms for Clustering Data},
  publisher = {Prentice Hall},
  year = {1988},
  author = {Jain, A.K. and Dubes, R.C.},
  address = {Englewood Cliffs, New Jersey 07632},
  groupsearch = {0},
  keywords = {algorithmic books}
}

@BOOK{jainBookClustering88,
  title = {Algorithms for Clustering Data},
  publisher = {Prentice-Hall},
  year = {1988},
  author = {A. Jain and R. Dubes},
  address = {Englewood Cliffs, NJ}
}

@ARTICLE{jain99data,
  author = {A. K. Jain and M. N. Murty and P. J. Flynn},
  title = {Data clustering: a review},
  journal = {ACM Computing Surveys},
  year = {1999},
  volume = {31},
  pages = {264--323},
  number = {3},
  url = {citeseer.ist.psu.edu/jain99data.html}
}

@INPROCEEDINGS{Jiang98,
  author = {J. Jiang and D. Conrath},
  title = {Semantic similarity based on corpus statistics and lexical taxonomy},
  booktitle = {Proceedings of the International Conference on Research in Computational
	Linguistics},
  year = {1998},
  address = {Taiwan},
  groupsearch = {0},
  keywords = {semantic distances}
}

@CONFERENCE{JoachimsTransSVM03,
  author = {T. Joachims},
  title = {Transductive Learning via Spectral Graph Partitioning},
  booktitle = {Int. Conf. Machine Learning},
  year = {2003}
}

@CONFERENCE{JoachimsXiAlpha00,
  author = {T. Joachims},
  title = {Estimating the Generalization Performance of a SVM Efficiently},
  booktitle = {Proc. Int. Conf. Machine Learning},
  year = {2000},
  publisher = {Morgan Kaufmann}
}

@CONFERENCE{JohKohPfl94,
  author = {G. John and R. Kohavi and K. Pfleger},
  title = {Irrelevant features and the subset selection problem},
  booktitle = {Machine Learning: Proc. 11th Int. Conf.},
  year = {1994},
  pages = {121 - 129},
  publisher = {Morgan Kaufmann}
}

@ARTICLE{PerttunenDIRECT93,
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  year = {1993},
  volume = {79},
  pages = {157 - 181},
  number = {1}
}

@ARTICLE{JonesEGO98,
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  title = {Efficient Global Optimization of Expensive Black-Box Functions},
  journal = {J. Global Optimization},
  year = {1998},
  volume = {13},
  pages = {455 - 492}
}

@ARTICLE{joslyn04categorizer,
  author = {C.A. Joslyn and S.M. Mniszewski and A. Fulmer and G. Heaton},
  title = {The Gene Ontology Categorizer},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {i169-i177},
  number = {1},
  doi = {10.1093/bioinformatics/bth921}
}

@ARTICLE{Kanehisa2008KEGG,
  author = {Kanehisa, M. and Araki, M. and Goto, S. and Hattori, M. and Hirakawa,
	M. and Itoh, M. and Katayama, T. and Kawashima, S. and Okuda, S.
	and Tokimatsu, T. and Yamanishi, Y.},
  title = {KEGG for linking genomes to life and the environment},
  journal = {Nucleic Acids Res.},
  year = {2008},
  volume = {36},
  pages = {D480 - D484},
  owner = {froehlih},
  timestamp = {2008.03.17}
}

@CONFERENCE{KannanSpectral00,
  author = {R. Kannan and S. Vempala},
  title = {On clusterings - good, bad and spectral},
  booktitle = {Proc. Symp. Found. Comp. Sci.},
  year = {2000},
  pages = {367 - 377}
}

@ARTICLE{KansyHIA98,
  author = {M. Kansy and F. Senner and K. Gubernator},
  title = {Physicochemical High Throughput Screening: Parallel Artificial Membrane
	Permeation Assay in the Description of Passive Absorption Processes},
  journal = {J. Med. Chem.},
  year = {1998},
  volume = {41},
  pages = {1007 - 1010}
}

@INCOLLECTION{KashimaSchoelkpfbook04,
  author = {H. Kashima and K. Tsuda and A. Inokuchi},
  title = {Kernels for Graphs},
  booktitle = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year = {2004},
  editor = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  pages = {155 - 170},
  address = {Cambridge, MA}
}

@CONFERENCE{Kashima03,
  author = {H. Kashima and K. Tsuda and A. Inokuchi},
  title = {Marginalized Kernels Between Labeled Graphs},
  booktitle = {Proc. 20th Int. Conf. on Machine Learning},
  year = {2003}
}

@BOOK{Kaufman1990ClusteringBook,
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  publisher = {Wiley},
  year = {1990},
  author = {L. Kaufman and P. Rousseeuw},
  address = {New York},
  owner = {froehlih},
  timestamp = {2007.02.07}
}

@ARTICLE{Kersey2004IPI,
  author = {D. Kersey and J. Duarte and A. Williams and Y. Karavidopoulou and
	E. Birney and R. Apweiler},
  title = {{The International Protein Index: an integrated database for proteomics
	experiments}},
  journal = {Proteomics},
  year = {2004},
  volume = {4},
  pages = {1985 - 1988},
  number = {7},
  owner = {froehlih},
  timestamp = {2008.03.17}
}

@ARTICLE{KimWahba71,
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}

@CONFERENCE{KirRen92,
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  title = {{A practical approach to feature selection}},
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  pages = {249 - 256}
}

@ARTICLE{Kirkpatrick1983SimAnn,
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  year = {1983},
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  pages = {671 - 680},
  number = {4598},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{Kohavi97,
  author = {R. Kohavi and G. John},
  title = {{Wrappers for Feature Subset Selection}},
  journal = {Artificial Intelligence},
  year = {1997},
  volume = {97},
  pages = {273 - 324},
  number = {12}
}

@BOOK{KohonenSOMs1995,
  title = {Self Organizing Maps},
  publisher = {Springer},
  year = {1995},
  author = {T. Kohonen},
  address = {Berlin},
  owner = {froehlih},
  timestamp = {2006.10.26}
}

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  title = {{Estimating attributes: Analysis and extensions of RELIEF}},
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  pages = {171 - 182}
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@ARTICLE{KonHon97,
  author = {I. Kononenko and S. J. Hong},
  title = {{Attribute Selection for Modeling}},
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  year = {1997},
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  pages = {181 - 195},
  number = {2 - 3}
}

@CONFERENCE{KrishnaJCFO003,
  author = {B. Krishnapuram and L. Carin and A. Hartemink},
  title = {Joint classifier and feature optimization for cancer diagnosis using
	gene expression data},
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  year = {2003},
  pages = {167 - 175},
  publisher = {ACM Press, NY}
}

@INPROCEEDINGS{kruskal56,
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	problem},
  booktitle = {Proc. Amer. Math. Soc.},
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@CONFERENCE{KubinyiReview04,
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  title = {Changing Paradigms in Drug Discovery},
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  year = {2004},
  editor = {M. Hicks et al.},
  pages = {51 - 72},
  address = {Berlin},
  publisher = {Logos-Verlag}
}

@ARTICLE{KubinyiDrugResearch03,
  author = {H. Kubinyi},
  title = {{Drug research: myths, hype and reality}},
  journal = {Nature Reviews: Drug Discovery},
  year = {2003},
  volume = {2},
  pages = {665-668},
  groupsearch = {0},
  pdf = {kub03.pdf},
  url = {http://home.t-online.de/home/kubinyi/nrdd-pub-08-03.pdf}
}

@ARTICLE{KubinyiQSARHistory02,
  author = {H. Kubinyi},
  title = {{F}rom {N}arcosis to {H}yperspace: {T}he {H}istory of {QSAR}},
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  year = {2002},
  volume = {21},
  pages = {348-356},
  groupsearch = {0},
  pdf = {kub02.pdf}
}

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@CONFERENCE{KKTTheorem51,
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  year = {1951},
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@INPROCEEDINGS{kurhekar02,
  author = {Kurhekar, M.P. and Adak, S. and Jhunjhunwala, S. and Raghupathy,
	K.},
  title = {Genome-wide pathway analysis and visulization using gene expression
	data},
  booktitle = {Proceedings of the Pacific Symposium on Biocomputing},
  year = {2002},
  pages = {462-473},
  groupsearch = {0},
  keywords = {pathway scoring methods}
}

@ARTICLE{KwokEvidenceFrameWorkSVM00,
  author = {J. Kwok},
  title = {The Evidence Framework Applied to Support Vector Machines},
  journal = {IEEE Transactions on Neural Networks},
  year = {2000},
  volume = {11},
  pages = {1162 - 1173},
  number = {5}
}

@ARTICLE{laiKCCA00,
  author = {P. Lai and C. Fyfe},
  title = {Kernel and nonlinear canonical correlation analysis},
  journal = {Int. Journal of Neural Systems},
  year = {2000},
  volume = {10},
  pages = {365 - 377},
  number = {5}
}

@ARTICLE{Cris04,
  author = {G. Lanckriet and N. Cristianini and P. Bartlett and L. El Ghaoui
	and M. Jordan},
  title = {Learning the Kernel Matrix with Semidefinite Programming},
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}

@ARTICLE{LangeStability,
  author = {Tilman Lange and Volker Roth and Mikio L. Braun and Joachim M. Buhmann},
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  issn = {0899-7667},
  publisher = {MIT Press}
}

@CONFERENCE{KwokBayesianSVR01,
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  year = {2001},
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}

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}

@ARTICLE{lee04,
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}

@ARTICLE{lenhard01genelynx,
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@INCOLLECTION{LeslieStringKernels04,
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  year = {2004},
  editor = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  pages = {95 - 112},
  address = {Cambridge, MA}
}

@ARTICLE{Liao2004localFDR,
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  title = {A mixture model for estimating the local false discovery rate in
	DNA microarray analysis},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {2694 - 2701},
  number = {16},
  owner = {froehlih},
  timestamp = {2007.02.02}
}

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@INPROCEEDINGS{Lord03,
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  title = {Semantic Similarity Measures as Tools for Exploring the Gene Ontology},
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}

@ARTICLE{Lord02,
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	between sequence and annotation},
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  year = {2002},
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  title = {{Analysis of temporal gene expression profiles: clustering by simulated
	annealing and determining the optimal number of clusters}},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {405-414},
  number = {5},
  abstract = {Motivation: Cluster analysis of genome-wide expression data from DNA
	microarray hybridization studies has proved to be a useful tool for
	identifying biologically relevant groupings of genes and samples.
	In the present paper, we focus on several important issues related
	to clustering algorithms that have not yet been fully studied. Results:
	We describe a simple and robust algorithm for the clustering of temporal
	gene expression profiles that is based on the simulated annealing
	procedure. In general, this algorithm guarantees to eventually find
	the globally optimal distribution of genes over clusters. We introduce
	an iterative scheme that serves to evaluate quantitatively the optimal
	number of clusters for each specific data set. The scheme is based
	on standard approaches used in regular statistical tests. The basic
	idea is to organize the search of the optimal number of clusters
	simultaneously with the optimization of the distribution of genes
	over clusters. The efficiency of the proposed algorithm has been
	evaluated by means of a reverse engineering experiment, that is,
	a situation in which the correct distribution of genes over clusters
	is known a priori. The employment of this statistically rigorous
	test has shown that our algorithm places greater than 90% genes into
	correct clusters. Finally, the algorithm has been tested on real
	gene expression data (expression changes during yeast cell cycle)
	for which the fundamental patterns of gene expression and the assignment
	of genes to clusters are well understood from numerous previous studies.
	Availability: The source code of the program implementing the algorithm
	is available upon request from the authors. Contact: alex_lukashin@biogen.com},
  doi = {10.1093/bioinformatics/17.5.405},
  eprint = {http://bioinformatics.oxfordjournals.org/cgi/reprint/17/5/405.pdf},
  url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/17/5/405}
}

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  year = {2007},
  volume = {23},
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  owner = {froehlih},
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  timestamp = {2006.11.28}
}

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@ARTICLE{Peer2001BayesNet,
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}

@ARTICLE{Peer06Minreg,
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	Networks in Yeast and Mammals}},
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}

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  timestamp = {2008.04.23},
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}

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}

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  number = {Database issue},
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	is a curated non-redundant collection of sequences representing genomes,
	transcripts and proteins. The database includes 3774 organisms spanning
	prokaryotes, eukaryotes and viruses, and has records for 2,879,860
	proteins (RefSeq release 19). RefSeq records integrate information
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	and its features. Annotations include coding regions, conserved domains,
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	Protein; Genome; Internet; National Library of Medicine (U.S.); Quality
	Control; RNA, Messenger; Reference Standards; Sequence Analysis,
	DNA; Sequence Analysis, Protein; Sequence Analysis, RNA; United States;
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}

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}

@ARTICLE{raychaudhuri03,
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	of a gene group},
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  year = {2003},
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  number = {3},
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}

@CONFERENCE{Ray00,
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  title = {Dimensionality Reduction Using Genetic Algorithms},
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  year = {2000}
}

@ARTICLE{RaymondGraphSimilarity02,
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  year = {2002},
  volume = {45},
  pages = {631 - 644},
  number = {6}
}

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	its application to problems of ambigiguity in natural language},
  journal = {Journal of Artificial Intelligence Research},
  year = {1999},
  volume = {11},
  pages = {95-130},
  address = {Montreal},
  groupsearch = {0},
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}

@INPROCEEDINGS{Resnik95,
  author = {Resnik, P.},
  title = {Using Information Content to Evaluate Semantic Similarity in a Taxonomy},
  booktitle = {Proceedings of the 14th International Joint Conference on Artificial
	Intelligence},
  year = {1995},
  volume = {1},
  pages = {448-453},
  address = {Montreal},
  groupsearch = {0},
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}

@CONFERENCE{KellyEditDistanceGraph03,
  author = {A. Robes-Kelly and E. Hancock},
  title = {{Edit Distance From Graph Spectra}},
  booktitle = {Proc. 9th IEEE Int. Conf. Comp. Vis.},
  year = {2003},
  volume = {1},
  pages = {234 - 241}
}

@ARTICLE{robinson03,
  author = {Robinson, P.N and Wollstein, A. and B?hme U. and Beattie B.},
  title = {Ontologizing gene-expression microarray data: characterizing clusters
	with Gene Ontology},
  journal = {Bioinformatics},
  year = {2003},
  volume = {20},
  pages = {979-981},
  number = {6},
  doi = {10.1093/bioinformatics/bth040}
}

@ARTICLE{Rogers2005DependencyNetworks,
  author = {S. Rogers and M. Girolami},
  title = {A Bayesian regression approach to the inference of regulatory networks
	from gene expression data},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {3131 - 3137},
  number = {14},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@CONFERENCE{rosales03learning,
  author = {R. Rosales and B. Frey},
  title = {Learning generative models of affinity matrices},
  booktitle = {19th Conf. on Uncertainty in Artificial Intelligence (UAI)},
  year = {2003},
  volume = {8}
}

@ARTICLE{Rosenblatt58,
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	Organization in the Brain}},
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}

@ARTICLE{RosipalKPLS01,
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	Space},
  journal = {J. Machine Learning Research},
  year = {2001},
  volume = {2},
  pages = {97 - 123}
}

@CONFERENCE{roth-resampling,
  author = {Volker Roth and Tilman Lange and Mikio Braun and Joachim Buhmann},
  title = {A Resampling Approach to Cluster Validation},
  booktitle = {Proc. Comput. Statistics: 15th Symposium Held in Berlin},
  year = {2002},
  pages = {123 - 128},
  url = {citeseer.ist.psu.edu/article/roth02resampling.html}
}

@ARTICLE{rousseeuw87,
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	of cluster analysis},
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  pages = {53-65}
}

@ARTICLE{Rung2002DisruptionNetworks,
  author = {Rung, J. and Schlitt, T. and Brazma, A. and Freivalds, K. and Vilo,
	J.},
  title = {Building and analysing genome-wide gene disruption networks},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18},
  pages = {S202 - S210},
  number = {Suppl 2},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@BOOK{RusNor95,
  title = {Artificial Intelligence - A Modern Approach},
  publisher = {Prentice Hall Inc.},
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}

@ARTICLE{Sachs2005BayesNet,
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	data},
  journal = {Science},
  year = {2005},
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  number = {5721},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@CONFERENCE{SanCumCruz02,
  author = {S. Salcedo-Sanz and M. Prado-Cumplido and F. Perez-Cruz and C. Bousono-Calzon},
  title = {{Feature Selection via Genetic Optimization}},
  booktitle = {Proc. Int. Conf. Artifical Neural Networks 2002},
  year = {2002},
  pages = {547 - 552}
}

@CONFERENCE{SchBurVap95,
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}

@ARTICLE{SchnuSVM00,
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  title = {New support vector algorithms},
  journal = {Neural Computation},
  year = {2000},
  volume = {12},
  pages = {1207 - 1245}
}

@BOOK{SchSmo02,
  title = {{Learning with Kernels}},
  publisher = {MIT Press},
  year = {2002},
  author = {B. Sch\"olkopf and A. J. Smola},
  address = {Cambridge, MA}
}

@BOOK{SchTsudaBioInfBook04,
  title = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year = {2004},
  author = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  address = {Cambridge, MA}
}

@ARTICLE{Schlicker2006GOFuncSim,
  author = {Andreas Schlicker and Francisco S Domingues and Jörg Rahnenführer
	and Thomas Lengauer},
  title = {{A new measure for functional similarity of gene products based on
	Gene Ontology}},
  journal = {BMC Bioinformatics},
  year = {2006},
  volume = {7},
  pages = {302},
  owner = {froehlih},
  timestamp = {2007.12.05}
}

@INPROCEEDINGS{Schohn00ActiveLearningSVM,
  author = {Greg Schohn and David Cohn},
  title = {Less is More: {A}ctive Learning with Support Vector Machines},
  booktitle = {Proc. 17th International Conf. on Machine Learning},
  year = {2000},
  pages = {839 - 846},
  publisher = {Morgan Kaufmann, San Francisco, CA},
  url = {citeseer.ist.psu.edu/schohn00less.html}
}

@ARTICLE{shah04,
  author = {Shah, N.H. and Fedoroff, N.V.},
  title = {{CLENCH}: a program for calculating {C}luster {EN}ri{CH}ment using
	{G}ene {O}ntology},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {1196-1197},
  number = {7},
  doi = {10.1093/bioinformatics/bth056}
}

@BOOK{CrisTaylorKernelMethods04,
  title = {Kernel Methods for Pattern Analysis},
  publisher = {Cambridge University Press},
  year = {2004},
  author = {J. Shawe-Taylor and N. Cristianini},
  address = {Cambridge, UK}
}

@ARTICLE{shi00normalized,
  author = {J. Shi and J. Malik},
  title = {Normalized Cuts and Image Segmentation},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2000},
  volume = {22},
  pages = {888-905},
  number = {8}
}

@BOOK{Siek2002BoostGraphLibrary,
  title = {The Boost Graph Library: User Guide and Reference Manual},
  publisher = {Addison-Wesley, Pearson Education Inc.},
  year = {2002},
  author = {Jeremy G. Siek and Lie-Quan Lee and Andrew Lumsdaine},
  owner = {froehlih},
  timestamp = {2008.04.01}
}

@TECHREPORT{SmolaSVRTut98,
  author = {A. Smola and B. Sch\"olkopf},
  title = {A Tutorial on Support Vector Regression},
  institution = {NeuroCOLT2 Technical Report Series},
  year = {1998},
  number = {NC2-TR-1998-030}
}

@ARTICLE{Smith2004Limma,
  author = {G. Smyth},
  title = {{Linear models and empirical Bayes methods for assessing differential
	expression in microarray experiments}},
  journal = {Statistical Applications in Genetics and Molecular Biology},
  year = {2004},
  volume = {3},
  number = {1},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{sohler04,
  author = {Sohler, F. and Hanisch, D. and Zimmer, R.},
  title = {New methods for joint analysis of biological networks and expression
	data},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {1517-1521},
  number = {10},
  doi = {10.1093/bioinformatics/bth112}
}

@CONFERENCE{FroeGO05,
  author = {N. Speer and H. Fr\"ohlich and C. Spieth and A. Zell},
  title = {Functional Grouping of Genes Using Spectral Clustering and Gene Ontology},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2005},
  pages = {298 - 303}
}

@INPROCEEDINGS{speer05GOSlim,
  author = {N. Speer and H. Fr{\"o}hlich and C. Spieth and A. Zell},
  title = {Functional Distances for Genes Based on GO Feature Maps and their
	Application to Clustering},
  booktitle = {Proc. IEEE Symp. on Comp. Intel. in Bioinf. and Comp. Biology (CIBCB
	2005) },
  year = {2005},
  pages = {142 - 149},
  address = {San Diego, USA},
  publisher = {IEEE Press}
}

@ARTICLE{Stossi2006ESR1CCNG2,
  author = {F. Stossi and V. Likhite and J. Katzenellenbogen and B. Katzenellenbogen},
  title = {Estrogen-occupied Estrogen Receptor Represses Cyclin G2 Gene Expression
	and Recruits a Repressor Complex and Cyclin G2 Promoter},
  journal = {The Journal of Biological Chemistry},
  year = {2006},
  volume = {281},
  pages = {16272 - 16287},
  number = {24},
  owner = {froehlih},
  timestamp = {2007.11.16}
}

@ARTICLE{SEDUMI99,
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  title = {Using SeDuMi1.02 a MATLAB toolbox for optimization over symmetric
	cones},
  journal = {Optimization Methods and Software},
  year = {1999},
  volume = {11/12},
  pages = {625 - 653},
  number = {1 - 4}
}

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  title = {Uniform crossover in genetic algorithms},
  booktitle = {Proceedings of the 3rd International Conference on Genetic Algorithms},
  year = {1989},
  pages = {2-9},
  groupsearch = {0},
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}

@ARTICLE{Takamori2001FST,
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  title = {{Effect of ACTH on the imipramine- and desipramine-induced decrease
	in duration of immobility time as measured in a rat forced swimming
	test]}},
  journal = {Life Sci.},
  year = {2001},
  volume = {69},
  pages = {1891-1896},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@INPROCEEDINGS{tamayo99,
  author = {Tamayo, P. and Slonim, D. and Mesirov, J.and Zhu, Q. and Kitareewan,
	S. and Dmitrovsky, E. and Lander, E.S. and Golub, T.R.},
  title = {Interpreting Patterns of Gene Expression with Self-Organizing Maps:
	Methods and application to hematopoietic differentiation},
  booktitle = {Proceedings of the National Academy of Sciences, USA},
  year = {1999},
  volume = {96},
  pages = {2907-2912},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@ARTICLE{tavazoie99,
  author = {Tavazoie, S. and Hughes, J.D. and Campbell, M.J. and Cho, R.J. and
	Church, G.M.},
  title = {Systematic Determination of Genetic Network Architecture},
  journal = {Nature Genetics},
  year = {1999},
  volume = {22},
  pages = {281-285},
  groupsearch = {0},
  keywords = {reviews}
}

@CONFERENCE{TesmerAMIFS04,
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  title = {AMIFS: Adaptive Feature Selection by Using Mutual Information},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2004},
  volume = {1},
  pages = {303 - 308}
}

@MISC{GOConsortiumWeb,
  author = {{The Gene Ontology Consortium}},
  howpublished = {http://www.geneontology.org},
  groupsearch = {0},
  keywords = {gene ontology}
}

@ARTICLE{GOConsortium04,
  author = {{The Gene Ontology Consortium}},
  title = {The Gene Ontology ({GO}) database and informatics resource},
  journal = {Nucleic Acids Research},
  year = {2004},
  volume = {32},
  pages = {D258-D261},
  groupsearch = {0},
  keywords = {gene ontology}
}

@ARTICLE{GOConsortium01,
  author = {{The Gene Ontology Consortium}},
  title = {Creating the gene ontology resource: design and implementation},
  journal = {Genome Research},
  year = {2001},
  volume = {11(8)},
  pages = {1425-1433},
  groupsearch = {0},
  keywords = {gene ontology}
}

@MISC{GOMySQL,
  author = {{The Gene Ontology Database}},
  howpublished = {\\http://www.godatabase.org/dev/database},
  groupsearch = {0},
  keywords = {gene ontology}
}

@BOOK{TikhonovRegularization77,
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  year = {1977},
  author = {A. Tikhonov and V. Arsenin}
}

@ARTICLE{Tong01ActiveLearningSVM,
  author = {S. Tong and D. Koller},
  title = {Support Vector Machine Active Learning with Applications to Text
	Classification},
  journal = {J. Machine Learning Research},
  year = {2001},
  volume = {2},
  pages = {45 - 66}
}

@ARTICLE{Tresch2007TransRed,
  author = {Achim Tresch and T. Bei{\ss}barth and H. S\"ultmann and R. Kuner
	and A. Poustka and A. Buness},
  title = {{Discrimination of direct and indirect interactions in a network
	of regulatory effects}},
  journal = {J Comput Biol},
  year = {2007},
  volume = {14},
  pages = {1217 - 1228},
  number = {9},
  month = {Nov},
  abstract = {The matter of concern are algorithms for the discrimination of direct
	from indirect regulatory effects from an interaction graph built
	up by error-prone measurements. Many of these algorithms can be cast
	as a rule for the removal of a single edge of the graph, such that
	the remaining graph is still consistent with the data. A set of mild
	conditions is given under which iterated application of such a rule
	leads to a unique minimal consistent graph. We show that three of
	the common methods for direct interactions search fulfill these conditions,
	thus providing a justification of their use. The main issues a reconstruction
	algorithm has to deal with, are the noise in the data, the presence
	of regulatory cycles, and the direction of the regulatory effects.
	We introduce a novel rule that, in contrast to the previously mentioned
	methods, simultaneously takes into account all these aspects. An
	efficient algorithm for the computation of the minimal graph is given,
	whose time complexity is cubic in the number of vertices of the graph.
	Finally, we demonstrate the utility of our method in a simulation
	study.},
  doi = {10.1089/cmb.2007.0085},
  owner = {froehlih},
  pmid = {17990974},
  timestamp = {2008.03.18},
  url = {http://dx.doi.org/10.1089/cmb.2007.0085}
}

@ARTICLE{Tresch2008NEMs,
  author = {A. Tresch and F. Markowetz},
  title = {{Structure Learning in Nested Effects Models}},
  journal = {{Statistical Applications in Genetics and Molecular Biology}},
  year = {2008},
  volume = {7},
  number = {1},
  note = {in Press},
  owner = {froehlih},
  timestamp = {2008.02.04}
}

@ARTICLE{Tusher2001SAM,
  author = {V. Tusher and R. Tibshirani and G. Chu},
  title = {Significance analysis of microarrays applied to the ionizing radiation
	response},
  journal = {Proc. Nat. Acad. Sci.},
  year = {2001},
  volume = {98},
  pages = {5116-5121},
  owner = {froehlih},
  timestamp = {2008.03.17}
}

@INCOLLECTION{Vaf93,
  author = {H. Vafaie and K. De Jong},
  title = {Evolutionary feature space transformation},
  booktitle = {Feature Extraction, Construction and Selection: a data mining perspective},
  publisher = {Kluwer},
  year = {1998},
  editor = {H. Liu and H. Motoda},
  pages = {307 - 323}
}

@ARTICLE{VandenbergheSDP96,
  author = {L. Vandenberghe and S. Boyd},
  title = {Semidefinite Programming},
  journal = {SIAM Review},
  year = {1996},
  volume = {38},
  pages = {49 - 95},
  number = {1}
}

@BOOK{Vapnik98,
  title = {Statistical Learning Theory},
  publisher = {John Wiley and Sons},
  year = {1998},
  author = {V. Vapnik},
  address = {New York}
}

@BOOK{Vapnik95,
  title = {{The Nature of Statistical Learning Theory}},
  publisher = {Springer},
  year = {1995},
  author = {V. Vapnik},
  address = {New York, NY}
}

@BOOK{Vapnik79,
  title = {Estimation of Dependencies Based on Empirical Data (in Russian)},
  publisher = {Nauka},
  year = {1979},
  author = {V. Vapnik},
  address = {Moscow}
}

@ARTICLE{VapCha00,
  author = {V. Vapnik and O. Chapelle},
  title = {{Bounds on error expectation for Support Vector Machines}},
  journal = {Neural Computation},
  year = {2000},
  volume = {12},
  number = {9}
}

@INCOLLECTION{VapChapSpanBound00,
  author = {V. Vapnik and O. Chapelle},
  title = {Bounds on Error Expectation for SVM},
  booktitle = {Advances in Large Margin Classifiers},
  publisher = {MIT Press},
  year = {2000},
  pages = {261 - 280},
  address = {Cambridge, MA}
}

@ARTICLE{VapCherUniConv68,
  author = {V. Vapnik and A. Chervonenkis},
  title = {Uniform convergence of frequencies of occurences of events to their
	probabilities},
  journal = {Dokl. Akad. Nauk SSSR},
  year = {1968},
  volume = {181},
  pages = {915 - 918}
}

@TECHREPORT{verma:03,
  author = {D. Verma and M. Meila},
  title = {A comparison of spectral clustering algorithms},
  institution = {University of Washington, CSE},
  year = {2003},
  number = {03-05-01}
}

@INCOLLECTION{SmolaStringTreeKernels04,
  author = {S. Vishwanathan and A. Smola},
  title = {{Fast Kernels for String and Tree Matching}},
  booktitle = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year = {2004},
  editor = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  pages = {113 - 130},
  address = {Cambridge, MA}
}

@ARTICLE{Wagner2002TransRed,
  author = {A. Wagner},
  title = {How to reconstruct a large genetic network from n gene perturbations
	in fewer than $n^2$ easy steps},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {1183 - 1197},
  number = {12},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@ARTICLE{WangData97,
  author = {R. Wang and Y. Fu and L. Lai},
  title = {A New Atom-Additive Method for Calculating Partition Coefficients},
  journal = {J. Chem. Inf. Comp. Sci.},
  year = {1997},
  volume = {37},
  pages = {615 - 621}
}

@ARTICLE{WashioDataMining03,
  author = {T. Washio and H. Motoda},
  title = {State of the Art of Graph-based Data Mining},
  journal = {SIGKDD Explorations Special Issue on Multi-Relational Data Mining},
  year = {2003},
  volume = {5},
  issue = {1}
}

@ARTICLE{WaterbeemdADMET03,
  author = {H. van de Waterbeemd and E. Gifford},
  title = {{{ADMET} \textit{{I}n {S}ilico} {M}odelling: {T}owards {P}rediction
	{P}aradise}?},
  journal = {{Nature Reviews: Drug Discovery}},
  year = {2003},
  volume = {2},
  pages = {192--204},
  abstract = {Following studies in the late 1990s that indicated that poor pharmacokinetics
	and toxicity were important causes of costly late-stage failures
	in drug development, it has become widely appreciated that these
	areas should be considered as early as possible in the drug discovery
	process. However, in recent years, combinatorial chemistry and high-throughput
	screening have significantly increased the number of compounds for
	which early data on absorption, distribution, metabolism, excretion
	(ADME) and toxicity (T) are needed, which has in turn driven the
	develop-ment of a variety of medium and high-throughput in vitro
	ADMET screens. Here, we describe how in silico approaches will further
	increase our ability to predict and model the most relevant pharma-cokinetic,
	metabolic and toxicity endpoints, thereby accelerating the drug discovery
	process.},
  groupsearch = {0},
  pdf = {wg03.pdf}
}

@ARTICLE{WegFroeHSCS05,
  author = {J. Wegner and H. Fr\"ohlich and H. Mielenz and A. Zell},
  title = {Data and Graph Mining in Chemical Space for ADME and Activity Data
	Sets},
  journal = {QSAR \& Comb. Sci.},
  year = {2005},
  note = {to appear}
}

@ARTICLE{WegFroe03,
  author = {J. Wegner and H. Fr\"ohlich and A. Zell},
  title = {{Feature Selection for Descriptor based Classificiation Models: Part
	I - Theory and GA-SEC Algorithm}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {44},
  pages = {921 - 930}
}

@ARTICLE{WegFroe03:2,
  author = {J. Wegner and H. Fr\"ohlich and A. Zell},
  title = {{Feature selection for Descriptor based Classification Models: Part
	II - Human Intestinal Absorption (HIA)}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {44},
  pages = {931 - 939}
}

@ARTICLE{Wegner03,
  author = {J. Wegner and A. Zell},
  title = {{Prediction of Aqueous Solubility and Partition Coefficient Optimized
	by a Genetic Algorithm Based Descriptor Selection Method}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {43},
  pages = {1077 - 1084},
  number = {3}
}

@MISC{joelib,
  author = {J???rg K. Wegner},
  title = {JOELIB - an Open Source chemoinformatics library},
  url = {http://sourceforge.net/projects/joelib/}
}

@PHDTHESIS{Wegner2006Diss,
  author = {J\"org K. Wegner},
  title = {{Data Mining und Graph Mining auf molekularen Graphen - Cheminformatik
	und molekulare Kodierungen fï¿½r ADME/Tox \& QSAR-Analysen}},
  school = {Eberhard-Karls Universit\"at T\"ubingen},
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  timestamp = {2006.10.17}
}

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@ARTICLE{yeung01_2,
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@ARTICLE{yeung01_1,
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  abstract = {TR4 is a new member of the nuclear hormone receptor family. This receptor
	is highly conserved in rat and human, but an in-frame insertion of
	19 amino acid residues in the amino-terminal (A/B) region was found
	in the human homolog, which we refer to as hTR4alpha1. By reverse
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	that is analogous in size and sequence to the reported rat TR4. RT-PCR
	analysis using total RNA derived from various rat tissues revealed
	a new rat TR4 transcript, referred to as rTR4alpha1, which is homologous
	to hTR4alpha1 since it contains the extra 19 amino acids in the A/B
	region. The two rat transcripts showed a differential tissue distribution.
	Analysis of the exon-intron organization of the hTR4 A/B region showed
	that the 19-amino-acid peptide insert in hTR4alpha1 was encoded by
	a separate exon, indicating that hTR4alpha1 and hTR4alpha2 transcripts
	were produced by the differential usage of the exon. RT-PCR analysis
	revealed that both hTR4alpha1 and hTR4alpha2 were detectable in brain,
	placenta, and ovary. In contrast, the human ovarian cancer cell line,
	PA1, failed to express hTR4alpha1. By fluorescence in situ hybridization,
	we have mapped the hTR4 gene to 3p25, a region deleted in some forms
	of cancer.},
  doi = {10.1006/geno.1996.0368},
  keywords = {Animals; Base Sequence; Brain; Chromosome Deletion; Chromosome Mapping;
	Chromosomes, Human, Pair 3; DNA Primers; Exons; Female; Humans; In
	Situ Hybridization, Fluorescence; Introns; Molecular Sequence Data;
	Neoplasms; Nerve Tissue Proteins; Organ Specificity; Ovary; Placenta;
	Polymerase Chain Reaction; Pregnancy; RNA, Messenger; Rats; Receptors,
	Cytoplasmic and Nuclear; Receptors, Steroid; Receptors, Thyroid Hormone;
	Transcription, Genetic; Variation (Genetics)},
  owner = {froehlih},
  pii = {S0888-7543(96)90368-7},
  pmid = {8661150},
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  url = {http://dx.doi.org/10.1006/geno.1996.0368}
}

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}

@MISC{gc,
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  keywords = {software}
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@MISC{humanCellCycleData,
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@MISC{sporulationData,
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  groupsearch = {0},
  keywords = {biological datasets}
}

@MISC{tumorCellData,
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  groupsearch = {0},
  keywords = {biological datasets}
}

