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@STRING{MPB = {Math. Program., Ser.~B}}

@STRING{SPRINGER = {Springer}}

@ARTICLE{Beisser2009,
  author = {Daniela Beisser and Gunnar W. Klau and Tobias Mueller and Marcus
	Dittrich},
  title = {BioNet: an R-package for the Functional Analysis of Biological Networks},
  journal = {Bioinformatics},
  year = {submitted},
  institution = {Department of Bioinformatics, Biocenter, University of Würzburg,
	Am Hubland, 97074 Würzburg, Germany.},
  language = {eng},
  medline-pst = {ppublish},
  pii = {btn161},
  pmid = {18586718}
}

@MANUAL{Carey2009,
  title = {RBGL: An interface to the BOOST graph library},
  author = {Vince Carey and Li Long and R. Gentleman},
  year = {2009},
  note = {R package version 1.20.0},
  url = {http://CRAN.R-project.org/package=RBGL}
}

@ARTICLE{Carey2005,
  author = {Vincent J Carey and Jeff Gentry and Elizabeth Whalen and Robert Gentleman},
  title = {{N}etwork structures and algorithms in {B}ioconductor.},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {135--136},
  number = {1},
  month = {Jan},
  abstract = {In this paper, we review the central concepts and implementations
	of tools for working with network structures in Bioconductor. Interfaces
	to open source resources for visualization (AT&T Graphviz) and network
	algorithms (Boost) have been developed to support analysis of graphical
	structures in genomics and computational biology. AVAILABILITY: Packages
	graph, Rgraphviz, RBGL of Bioconductor (www.bioconductor.org).},
  keywords = {Algorithms; Computer Graphics; Computer Simulation; Gene Expression
	Regulation; Information Storage and Retrieval; Internet; Models,
	Biological; Signal Transduction; Transcription Factors; User-Computer
	Interface},
  owner = {Marcus Dittrich},
  pii = {bth458},
  pmid = {15297301},
  timestamp = {2007.09.20}
}

@ARTICLE{Chiaretti2004,
  author = {Sabina Chiaretti and Xiaochun Li and Robert Gentleman and Antonella
	Vitale and Marco Vignetti and Franco Mandelli and Jerome Ritz and
	Robin Foa},
  title = {Gene expression profile of adult T-cell acute lymphocytic leukemia
	identifies distinct subsets of patients with different response to
	therapy and survival.},
  journal = {Blood},
  year = {2004},
  volume = {103},
  pages = {2771--2778},
  number = {7},
  month = {Apr},
  abstract = {Gene expression profiles were examined in 33 adult patients with T-cell
	acute lymphocytic leukemia (T-ALL). Nonspecific filtering criteria
	identified 313 genes differentially expressed in the leukemic cells.
	Hierarchical clustering of samples identified 2 groups that reflected
	the degree of T-cell differentiation but was not associated with
	clinical outcome. Comparison between refractory patients and those
	who responded to induction chemotherapy identified a single gene,
	interleukin 8 (IL-8), that was highly expressed in refractory T-ALL
	cells and a set of 30 genes that was highly expressed in leukemic
	cells from patients who achieved complete remission. We next identified
	19 genes that were differentially expressed in T-ALL cells from patients
	who either had a relapse or remained in continuous complete remission.
	A model based on the expression of 3 of these genes was predictive
	of duration of remission. The 3-gene model was validated on a further
	set of T-ALL samples from 18 additional patients treated on the same
	clinical protocol. This study demonstrates that gene expression profiling
	can identify a limited number of genes that are predictive of response
	to induction therapy and remission duration in adult patients with
	T-ALL.},
  institution = {Department of Medical Oncology, Dana-Farber Cancer Institute, Department
	of Medicine, Brigham and Women's Hospital, Harvard Medical School,
	Boston, MA 02115, USA.},
  keywords = {Adult; Base Sequence; DNA Primers; Gene Expression Profiling, methods;
	Gene Expression Regulation, Neoplastic, genetics; Humans; Leukemia-Lymphoma,
	Adult T-Cell, genetics/mortality/therapy; Models, Genetic; Oligonucleotide
	Array Sequence Analysis, methods; Predictive Value of Tests; Reproducibility
	of Results; Reverse Transcriptase Polymerase Chain Reaction, methods;
	Survival Rate; Time Factors; Tumor Cells, Cultured},
  language = {eng},
  medline-pst = {ppublish},
  owner = {dab60cg},
  pii = {2003-09-3243},
  pmid = {14684422},
  timestamp = {2009.11.06}
}

@ARTICLE{Csardi2006,
  author = {Gabor Csardi and Tamas Nepusz},
  title = {The igraph software package for complex network research},
  journal = {InterJournal},
  year = {2006},
  volume = {Complex Systems},
  pages = {1695},
  url = {http://igraph.sf.net}
}

@ARTICLE{Dittrich2008,
  author = {Marcus T Dittrich and Gunnar W Klau and Andreas Rosenwald and Thomas
	Dandekar and Tobias Müller},
  title = {Identifying functional modules in protein-protein interaction networks:
	an integrated exact approach.},
  journal = {Bioinformatics},
  year = {2008},
  volume = {24},
  pages = {i223--i231},
  number = {13},
  month = {Jul},
  __markedentry = {[bi]},
  abstract = {MOTIVATION: With the exponential growth of expression and protein-protein
	interaction (PPI) data, the frontier of research in systems biology
	shifts more and more to the integrated analysis of these large datasets.
	Of particular interest is the identification of functional modules
	in PPI networks, sharing common cellular function beyond the scope
	of classical pathways, by means of detecting differentially expressed
	regions in PPI networks. This requires on the one hand an adequate
	scoring of the nodes in the network to be identified and on the other
	hand the availability of an effective algorithm to find the maximally
	scoring network regions. Various heuristic approaches have been proposed
	in the literature. RESULTS: Here we present the first exact solution
	for this problem, which is based on integer-linear programming and
	its connection to the well-known prize-collecting Steiner tree problem
	from Operations Research. Despite the NP-hardness of the underlying
	combinatorial problem, our method typically computes provably optimal
	subnetworks in large PPI networks in a few minutes. An essential
	ingredient of our approach is a scoring function defined on network
	nodes. We propose a new additive score with two desirable properties:
	(i) it is scalable by a statistically interpretable parameter and
	(ii) it allows a smooth integration of data from various sources.
	We apply our method to a well-established lymphoma microarray dataset
	in combination with associated survival data and the large interaction
	network of HPRD to identify functional modules by computing optimal-scoring
	subnetworks. In particular, we find a functional interaction module
	associated with proliferation over-expressed in the aggressive ABC
	subtype as well as modules derived from non-malignant by-stander
	cells. AVAILABILITY: Our software is available freely for non-commercial
	purposes at http://www.planet-lisa.net.},
  institution = {Department of Bioinformatics, Biocenter, University of Würzburg,
	Am Hubland, 97074 Würzburg, Germany.},
  keywords = {Algorithms; Gene Expression Profiling, methods; Humans; Lymphoma,
	metabolism; Protein Interaction Mapping, methods; Proteome, metabolism;
	Signal Transduction; Systems Integration},
  language = {eng},
  medline-pst = {ppublish},
  owner = {bi},
  pii = {btn161},
  pmid = {18586718},
  timestamp = {2009.06.26}
}

@MANUAL{Gentleman2009,
  title = {graph: A package to handle graph data structures},
  author = {R. Gentleman and Elizabeth Whalen and W. Huber and S. Falcon},
  year = {2009},
  note = {R package version 1.22.2},
  url = {http://CRAN.R-project.org/package=graph}
}

@ARTICLE{Gentleman2004,
  author = {Robert C Gentleman and Vincent J Carey and Douglas M Bates and Ben
	Bolstad and Marcel Dettling and Sandrine Dudoit and Byron Ellis and
	Laurent Gautier and Yongchao Ge and Jeff Gentry and Kurt Hornik and
	Torsten Hothorn and Wolfgang Huber and Stefano Iacus and Rafael Irizarry
	and Friedrich Leisch and Cheng Li and Martin Maechler and Anthony
	J Rossini and Gunther Sawitzki and Colin Smith and Gordon Smyth and
	Luke Tierney and Jean Y H Yang and Jianhua Zhang},
  title = {{B}ioconductor: open software development for computational biology
	and bioinformatics.},
  journal = {Genome Biol},
  year = {2004},
  volume = {5},
  pages = {R80},
  number = {10},
  abstract = {The Bioconductor project is an initiative for the collaborative creation
	of extensible software for computational biology and bioinformatics.
	The goals of the project include: fostering collaborative development
	and widespread use of innovative software, reducing barriers to entry
	into interdisciplinary scientific research, and promoting the achievement
	of remote reproducibility of research results. We describe details
	of our aims and methods, identify current challenges, compare Bioconductor
	to other open bioinformatics projects, and provide working examples.},
  doi = {10.1186/gb-2004-5-10-r80},
  keywords = {Bayes Theorem, Biometry, Brain, Computational Biology, Computer Simulation,
	Extramural, Fourier Analysis, Humans, Image Enhancement, Internet,
	Magnetic Resonance Imaging, N.I.H., Non-P.H.S., P.H.S., Probability,
	Reproducibility of Results, Research Support, Software, U.S. Gov't,
	15461798},
  owner = {Marcus Dittrich},
  pii = {gb-2004-5-10-r80},
  pmid = {15461798},
  timestamp = {2007.09.20},
  url = {http://dx.doi.org/10.1186/gb-2004-5-10-r80}
}

@MANUAL{Huber2006,
  title = {estrogen: 2x2 factorial design exercise for the Bioconductor short
	course},
  author = {Wolfgang Huber and Robert Gentleman},
  year = {2006},
  note = {R package version 1.8.2}
}

@ARTICLE{Ideker2002,
  author = {Trey Ideker and Owen Ozier and Benno Schwikowski and Andrew F Siegel},
  title = {{D}iscovering regulatory and signalling circuits in molecular interaction
	networks.},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18 Suppl 1},
  pages = {S233--S240},
  abstract = {MOTIVATION: In model organisms such as yeast, large databases of protein-protein
	and protein-DNA interactions have become an extremely important resource
	for the study of protein function, evolution, and gene regulatory
	dynamics. In this paper we demonstrate that by integrating these
	interactions with widely-available mRNA expression data, it is possible
	to generate concrete hypotheses for the underlying mechanisms governing
	the observed changes in gene expression. To perform this integration
	systematically and at large scale, we introduce an approach for screening
	a molecular interaction network to identify active subnetworks, i.e.,
	connected regions of the network that show significant changes in
	expression over particular subsets of conditions. The method we present
	here combines a rigorous statistical measure for scoring subnetworks
	with a search algorithm for identifying subnetworks with high score.
	RESULTS: We evaluated our procedure on a small network of 332 genes
	and 362 interactions and a large network of 4160 genes containing
	all 7462 protein-protein and protein-DNA interactions in the yeast
	public databases. In the case of the small network, we identified
	five significant subnetworks that covered 41 out of 77 (53\%) of
	all significant changes in expression. Both network analyses returned
	several top-scoring subnetworks with good correspondence to known
	regulatory mechanisms in the literature. These results demonstrate
	how large-scale genomic approaches may be used to uncover signalling
	and regulatory pathways in a systematic, integrative fashion.},
  comment = {heinz 1.1},
  file = {:dis_reg_networks.pdf:PDF},
  institution = {Whitehead Institute for Biomedical Research, Cambridge, MA 02142,
	USA Institute for Systems Biology, Seattle, WA 98103, USA. trey@wi.mit.edu},
  keywords = {Algorithms; Computer Simulation; Gene Expression Profiling; Gene Expression
	Regulation; Models, Biological; Proteome; Signal Transduction; Yeasts},
  owner = {gunnar},
  pmid = {12169552},
  timestamp = {2007.08.31}
}

@ARTICLE{Ljubi'c2006,
  author = {I.~Ljubi{\'c} and R. Weiskircher and U. Pferschy and G.~W. Klau and
	P. Mutzel and M. Fischetti},
  title = {{A}n {A}lgorithmic {F}ramework for the {E}xact {S}olution of the
	{P}rize-{C}ollecting {S}teiner {T}ree {P}roblem},
  journal = MPB,
  year = {2006},
  volume = {105},
  pages = {427-449},
  number = {2-3},
  optmonth = {feb},
  publisher = SPRINGER
}

@ARTICLE{Pounds2003,
  author = {Stan Pounds and Stephan W Morris},
  title = {{E}stimating the occurrence of false positives and false negatives
	in microarray studies by approximating and partitioning the empirical
	distribution of p-values.},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  pages = {1236--1242},
  number = {10},
  month = {Jul},
  abstract = {MOTIVATION: The occurrence of false positives and false negatives
	in a microarray analysis could be easily estimated if the distribution
	of p-values were approximated and then expressed as a mixture of
	null and alternative densities. Essentially any distribution of p-values
	can be expressed as such a mixture by extracting a uniform density
	from it. RESULTS: The occurrence of false positives and false negatives
	in a microarray analysis could be easily estimated if the distribution
	of p-values were approximated and then expressed as a mixture of
	null and alternative densities. Essentially any distribution of p-values
	can be expressed as such a mixture by extracting a uniform density
	from it. AVAILABILITY: An S-plus function library is available from
	http://www.stjuderesearch.org/statistics.},
  keywords = {Adaptor Proteins, Signal Transducing; Algorithms; Animals; B-Lymphocytes;
	False Negative Reactions; False Positive Reactions; Gene Expression
	Profiling; Mice; Neoplasm Proteins; Oligonucleotide Array Sequence
	Analysis; Quality Control; Reproducibility of Results; Sensitivity
	and Specificity; Sequence Alignment; Sequence Analysis},
  owner = {Marcus Dittrich},
  pmid = {12835267},
  timestamp = {2007.09.07}
}

@ARTICLE{Prasad2009,
  author = {T. S Keshava Prasad and Kumaran Kandasamy and Akhilesh Pandey},
  title = {Human Protein Reference Database and Human Proteinpedia as discovery
	tools for systems biology.},
  journal = {Methods Mol Biol},
  year = {2009},
  volume = {577},
  pages = {67-79},
  __markedentry = {[dab60cg]},
  abstract = {Although high-throughput technologies used in biology have resulted
	in the accumulation of vast amounts of data in the literature, it
	is becoming difficult for individual investigators to directly benefit
	from this data because they are not easily accessible. Databases
	have assumed a crucial role in assimilating and storing information
	that could enable future discoveries. To this end, our group has
	developed two resources - Human Protein Reference Database (HPRD)
	and Human Proteinpedia - that provide integrated information pertaining
	to human proteins. These databases contain information on a number
	of features of proteins that have been discovered using various experimental
	methods. Human Proteinpedia was developed as a portal for community
	participation to annotate and share proteomic data using HPRD as
	the scaffold. It allows proteomic investigators to even share unpublished
	data and provides an effective medium for data sharing. As proteomic
	information reflects a direct view of cellular systems, proteomics
	is expected to complement other areas of biology such as genomics,
	transcriptomics, classical genetics, and chemical genetics in understanding
	the relationships among genome, gene functions, and living systems.},
  institution = {Institute of Bioinformatics, Bangalore, India.},
  language = {eng},
  medline-pst = {ppublish},
  owner = {dab60cg},
  pmid = {19718509},
  timestamp = {2009.12.04}
}

@ARTICLE{Rosenwald2002,
  author = {Andreas Rosenwald and George Wright and Wing C Chan and Joseph M
	Connors and Elias Campo and Richard I Fisher and Randy D Gascoyne
	and H. Konrad Müller-Hermelink and Erlend B Smeland and Jena M Giltnane
	and Elaine M Hurt and Hong Zhao and Lauren Averett and Liming Yang
	and Wyndham H Wilson and Elaine S Jaffe and Richard Simon and Richard
	D Klausner and John Powell and Patricia L Duffey and Dan L Longo
	and Timothy C Greiner and Dennis D Weisenburger and Warren G Sanger
	and Bhavana J Dave and James C Lynch and Julie Vose and James O Armitage
	and Emilio Montserrat and Armando López-Guillermo and Thomas M Grogan
	and Thomas P Miller and Michel LeBlanc and German Ott and Stein Kvaloy
	and Jan Delabie and Harald Holte and Peter Krajci and Trond Stokke
	and Louis M Staudt and Lymphoma/Leukemia Molecular Profiling Project},
  title = {{T}he use of molecular profiling to predict survival after chemotherapy
	for diffuse large-{B}-cell lymphoma.},
  journal = {N Engl J Med},
  year = {2002},
  volume = {346},
  pages = {1937--1947},
  number = {25},
  month = {Jun},
  abstract = {BACKGROUND: The survival of patients with diffuse large-B-cell lymphoma
	after chemotherapy is influenced by molecular features of the tumors.
	We used the gene-expression profiles of these lymphomas to develop
	a molecular predictor of survival. METHODS: Biopsy samples of diffuse
	large-B-cell lymphoma from 240 patients were examined for gene expression
	with the use of DNA microarrays and analyzed for genomic abnormalities.
	Subgroups with distinctive gene-expression profiles were defined
	on the basis of hierarchical clustering. A molecular predictor of
	risk was constructed with the use of genes with expression patterns
	that were associated with survival in a preliminary group of 160
	patients and was then tested in a validation group of 80 patients.
	The accuracy of this predictor was compared with that of the international
	prognostic index. RESULTS: Three gene-expression subgroups--germinal-center
	B-cell-like, activated B-cell-like, and type 3 diffuse large-B-cell
	lymphoma--were identified. Two common oncogenic events in diffuse
	large-B-cell lymphoma, bcl-2 translocation and c-rel amplification,
	were detected only in the germinal-center B-cell-like subgroup. Patients
	in this subgroup had the highest five-year survival rate. To identify
	other molecular determinants of outcome, we searched for individual
	genes with expression patterns that correlated with survival in the
	preliminary group of patients. Most of these genes fell within four
	gene-expression signatures characteristic of germinal-center B cells,
	proliferating cells, reactive stromal and immune cells in the lymph
	node, or major-histocompatibility-complex class II complex. We used
	17 genes to construct a predictor of overall survival after chemotherapy.
	This gene-based predictor and the international prognostic index
	were independent prognostic indicators. CONCLUSIONS: DNA microarrays
	can be used to formulate a molecular predictor of survival after
	chemotherapy for diffuse large-B-cell lymphoma.},
  keywords = {Antibiotics, Antineoplastic; Antineoplastic Combined Chemotherapy
	Protocols; Biopsy; Female; Gene Expression Profiling; Humans; Lymphoma,
	B-Cell; Lymphoma, Large-Cell, Diffuse; Male; Middle Aged; Oligonucleotide
	Array Sequence Analysis; Prognosis; Proportional Hazards Models;
	Retrospective Studies; Survival Analysis},
  owner = {Marcus Dittrich},
  pmid = {12075054},
  timestamp = {2007.09.20}
}

@ARTICLE{Shannon2003,
  author = {Paul Shannon and Andrew Markiel and Owen Ozier and Nitin S Baliga
	and Jonathan T Wang and Daniel Ramage and Nada Amin and Benno Schwikowski
	and Trey Ideker},
  title = {{C}ytoscape: a software environment for integrated models of biomolecular
	interaction networks.},
  journal = {Genome Res},
  year = {2003},
  volume = {13},
  pages = {2498--2504},
  number = {11},
  month = {Nov},
  abstract = {Cytoscape is an open source software project for integrating biomolecular
	interaction networks with high-throughput expression data and other
	molecular states into a unified conceptual framework. Although applicable
	to any system of molecular components and interactions, Cytoscape
	is most powerful when used in conjunction with large databases of
	protein-protein, protein-DNA, and genetic interactions that are increasingly
	available for humans and model organisms. Cytoscape's software Core
	provides basic functionality to layout and query the network; to
	visually integrate the network with expression profiles, phenotypes,
	and other molecular states; and to link the network to databases
	of functional annotations. The Core is extensible through a straightforward
	plug-in architecture, allowing rapid development of additional computational
	analyses and features. Several case studies of Cytoscape plug-ins
	are surveyed, including a search for interaction pathways correlating
	with changes in gene expression, a study of protein complexes involved
	in cellular recovery to DNA damage, inference of a combined physical/functional
	interaction network for Halobacterium, and an interface to detailed
	stochastic/kinetic gene regulatory models.},
  keywords = {Algorithms; Archaeal Proteins; Bacteriophage lambda; Computational
	Biology; Halobacterium; Internet; Models, Biological; Neural Networks
	(Computer); Phenotype; Software; Software Design; Stochastic Processes},
  owner = {Marcus Dittrich},
  pii = {13/11/2498},
  pmid = {14597658},
  timestamp = {2007.09.20}
}

@INCOLLECTION{Smyth2005,
  author = {Gordon K. Smyth},
  title = {Limma: linear models for microarray data},
  booktitle = {Bioinformatics and Computational Biology Solutions using R and Bioconductor},
  publisher = {Springer},
  year = {2005},
  editor = {R. Gentleman and V. Carey and S. Dudoit and R. Irizarry, W. Huber},
  pages = {397--420},
  address = {New York}
}

@ARTICLE{Smyth2004,
  author = {Gordon K Smyth},
  title = {{L}inear models and empirical {B}ayes methods for assessing differential
	expression in microarray experiments.},
  journal = {Statistical Applications in Genetics and Molecular Biology},
  year = {2004},
  volume = {3 (1)},
  pages = {Article 3},
  owner = {Marcus Dittrich},
  timestamp = {2007.09.20}
}

