
@ARTICLE{Carrivick2006,
  author = {L. Carrivick and S. Rogers and J. Clark and C. Campbell and M. Girolami
	and C. Cooper},
  title = {Identification of prognostic signatures in breast cancer microarray
	data using Bayesian techniques.},
  journal = {J R Soc Interface},
  year = {2006},
  volume = {3},
  pages = {367--381},
  number = {8},
  month = {Jun},
  abstract = {We apply a new Bayesian data analysis technique (latent process decomposition)
	to four recent microarray datasets for breast cancer. Compared to
	hierarchical cluster analysis, for example, this technique has advantages
	such as objective assessment of the optimal number of sample or gene
	clusters in the data, penalization of overcomplex models fitting
	to noise in the data and a common latent space of explanatory variables
	for samples and genes. Our analysis provides a clearer insight into
	these datasets, enabling assignment of patients to one of four principal
	processes, each with a distinct clinical outcome. One process is
	indolent and associated with under-expression across a number of
	genes associated with tumour growth. One process is associated with
	over expression of GRB7 and ERBB2. The most aggressive process is
	associated with abnormal expression of transcription factor genes,
	including members of the FOX family of transcription factor genes.},
  doi = {10.1098/rsif.2005.0093},
  pii = {L38M4771988U6521},
  pmid = {16849266},
  url = {http://dx.doi.org/10.1098/rsif.2005.0093}
}

@ARTICLE{Girolami2006,
  author = {Mark Girolami and Simon Rogers},
  title = {Variational Bayesian Multinomial Probit Regression with Gaussian
	Process Priors},
  journal = {Neural Computation},
  year = {2006},
  volume = {18},
  pages = {1790--1817}
}

@ARTICLE{Kote-Jarai2006,
  author = {Zsofia Kote-Jarai and Lucy Matthews and Ana Osorio and Susan Shanley
	and Ian Giddings and Francois Moreews and Imogen Locke and D. Gareth
	Evans and Diana Eccles and Carrier Clinic Collaborators and Richard
	D Williams and Mark Girolami and Colin Campbell and Ros Eeles},
  title = {Accurate prediction of BRCA1 and BRCA2 heterozygous genotype using
	expression profiling after induced DNA damage.},
  journal = {Clin Cancer Res},
  year = {2006},
  volume = {12},
  pages = {3896--3901},
  number = {13},
  month = {Jul},
  abstract = {PURPOSE: In this study, the differential gene expression changes following
	radiation-induced DNA damage in healthy cells from BRCA1/BRCA1 mutation
	carriers have been compared with controls using high-density microarray
	technology. We aimed to establish if BRCA1/BRCA2 mutation carriers
	could be distinguished from noncarriers based on expression profiling
	of normal cells. EXPERIMENTAL DESIGN: Short-term primary fibroblast
	cultures were established from skin biopsies from 10 BRCA1 and 10
	BRCA2 mutation carriers and 10 controls, all of whom had previously
	had breast cancer. The cells were subjected to 15 Gy ionizing irradiation
	to induce DNA damage. RNA was extracted from all cell cultures, preirradiation
	and at 1 hour postirradiation. For expression profiling, 15 K spotted
	cDNA microarrays manufactured by the Cancer Research UK DNA Microarray
	Facility were used. Statistical feature selection was used with a
	support vector machine (SVM) classifier to determine the best feature
	set for predicting BRCA1 or BRCA2 heterozygous genotype. To investigate
	prediction accuracy, a nonprobabilistic classifier (SVM) and a probabilistic
	Gaussian process classifier were used. RESULTS: In the task of distinguishing
	BRCA1 and BRCA2 mutation carriers from noncarriers and from each
	other following radiation-induced DNA damage, the SVM achieved 90\%,
	and the Gaussian process classifier achieved 100\% accuracy. This
	effect could not be achieved without irradiation. In addition, the
	SVM identified a set of BRCA genotype predictor genes. CONCLUSIONS:
	We conclude that after irradiation-induced DNA damage, BRCA1 and
	BRCA2 mutation carrier cells have a distinctive expression phenotype,
	and this may have a future role in predicting genotypes, with application
	to clinical detection and classification of mutations.},
  doi = {10.1158/1078-0432.CCR-05-2805},
  pii = {12/13/3896},
  pmid = {16818684},
  url = {http://dx.doi.org/10.1158/1078-0432.CCR-05-2805}
}

@INCOLLECTION{Neal1998,
  author = {R. Neal},
  title = {Regression and classification using Gaussian process priors},
  booktitle = {Bayesian Statistics},
  publisher = {Oxford University Press},
  year = {1998},
  editor = {A.P. Dawid, M. Bernardo, J.O. Berger and A.F.M. Smith},
  volume = {6},
  pages = {475--501}
}

@ARTICLE{Rogers2005a,
  author = {Simon Rogers and Mark 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},
  month = {Jul},
  abstract = {MOTIVATION: There is currently much interest in reverse-engineering
	regulatory relationships between genes from microarray expression
	data. We propose a new algorithmic method for inferring such interactions
	between genes using data from gene knockout experiments. The algorithm
	we use is the Sparse Bayesian regression algorithm of Tipping and
	Faul. This method is highly suited to this problem as it does not
	require the data to be discretized, overcomes the need for an explicit
	topology search and, most importantly, requires no heuristic thresholding
	of the discovered connections. RESULTS: Using simulated expression
	data, we are able to show that this algorithm outperforms a recently
	published correlation-based approach. Crucially, it does this without
	the need to set any ad hoc threshold on possible connections.},
  doi = {10.1093/bioinformatics/bti487},
  keywords = {Algorithms; Bayes Theorem; Gene Expression Profiling; Gene Expression
	Regulation; Likelihood Functions; Models, Genetic; Models, Statistical;
	Oligonucleotide Array Sequence Analysis; Regression Analysis; Signal
	Transduction; Transcription Factors},
  pii = {bti487},
  pmid = {15879452},
  url = {http://dx.doi.org/10.1093/bioinformatics/bti487}
}

@ARTICLE{Rogers2005,
  author = {Simon Rogers and Mark Girolami and Colin Campbell and Rainer Breitling},
  title = {The latent process decomposition of cDNA microarray data sets.},
  journal = {IEEE/ACM Trans Comput Biol Bioinform},
  year = {2005},
  volume = {2},
  pages = {143--156},
  number = {2},
  abstract = {We present a new computational technique (a software implementation,
	data sets, and supplementary information are available at http://www.enm.bris.ac.uk/lpd/)
	which enables the probabilistic analysis of cDNA microarray data
	and we demonstrate its effectiveness in identifying features of biomedical
	importance. A hierarchical Bayesian model, called Latent Process
	Decomposition (LPD), is introduced in which each sample in the data
	set is represented as a combinatorial mixture over a finite set of
	latent processes, which are expected to correspond to biological
	processes. Parameters in the model are estimated using efficient
	variational methods. This type of probabilistic model is most appropriate
	for the interpretation of measurement data generated by cDNA microarray
	technology. For determining informative substructure in such data
	sets, the proposed model has several important advantages over the
	standard use of dendrograms. First, the ability to objectively assess
	the optimal number of sample clusters. Second, the ability to represent
	samples and gene expression levels using a common set of latent variables
	(dendrograms cluster samples and gene expression values separately
	which amounts to two distinct reduced space representations). Third,
	in constrast to standard cluster models, observations are not assigned
	to a single cluster and, thus, for example, gene expression levels
	are modeled via combinations of the latent processes identified by
	the algorithm. We show this new method compares favorably with alternative
	cluster analysis methods. To illustrate its potential, we apply the
	proposed technique to several microarray data sets for cancer. For
	these data sets it successfully decomposes the data into known subtypes
	and indicates possible further taxonomic subdivision in addition
	to highlighting, in a wholly unsupervised manner, the importance
	of certain genes which are known to be medically significant. To
	illustrate its wider applicability, we also illustrate its performance
	on a microarray data set for yeast.},
  doi = {10.1109/TCBB.2005.29},
  keywords = {Algorithms; Artificial Intelligence; Bayes Theorem; Cluster Analysis;
	Database Management Systems; Databases, Genetic; Gene Expression
	Profiling; Information Storage and Retrieval; Models, Genetic; Models,
	Statistical; Oligonucleotide Array Sequence Analysis; Pattern Recognition,
	Automated},
  pmid = {17044179},
  url = {http://dx.doi.org/10.1109/TCBB.2005.29}
}

@ARTICLE{Rogers2007,
  author = {Simon Rogers and Raya Khanin and Mark Girolami},
  title = {Bayesian model-based inference of transcription factor activity.},
  journal = {BMC Bioinformatics},
  year = {2007},
  volume = {8 Suppl 2},
  pages = {S2},
  abstract = {BACKGROUND: In many approaches to the inference and modeling of regulatory
	interactions using microarray data, the expression of the gene coding
	for the transcription factor is considered to be an accurate surrogate
	for the true activity of the protein it produces. There are many
	instances where this is inaccurate due to post-translational modifications
	of the transcription factor protein. Inference of the activity of
	the transcription factor from the expression of its targets has predominantly
	involved linear models that do not reflect the nonlinear nature of
	transcription. We extend a recent approach to inferring the transcription
	factor activity based on nonlinear Michaelis-Menten kinetics of transcription
	from maximum likelihood to fully Bayesian inference and give an example
	of how the model can be further developed. RESULTS: We present results
	on synthetic and real microarray data. Additionally, we illustrate
	how gene and replicate specific delays can be incorporated into the
	model. CONCLUSION: We demonstrate that full Bayesian inference is
	appropriate in this application and has several benefits over the
	maximum likelihood approach, especially when the volume of data is
	limited. We also show the benefits of using a non-linear model over
	a linear model, particularly in the case of repression.},
  doi = {10.1186/1471-2105-8-S2-S2},
  keywords = {Algorithms; Artificial Intelligence; Bayes Theorem; Computer Simulation;
	Models, Biological; Signal Transduction; Trans-Activation (Genetics);
	Transcription Factors},
  pii = {1471-2105-8-S2-S2},
  pmid = {17493251},
  url = {http://dx.doi.org/10.1186/1471-2105-8-S2-S2}
}

