CiteULike: Group: Bioinformatics - with tag bayesian

Integration of microarray and textual data improves the prognosis prediction of breast, lung and ovarian cancer patients.

O Gevaert — 2008-04-07T13:48:59-00:00

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2008), pp. 279-290.

Microarray data are notoriously noisy such that models predicting clinically relevant outcomes often contain many false positive genes. Integration of other data sources can alleviate this problem and enhance gene selection and model building. Probabilistic models provide a natural solution to integrate information by using the prior over model space. We investigated if the use of text information from PUBMED abstracts in the structure prior of a Bayesian network could improve the prediction of the prognosis in cancer. Our results show that prediction of the outcome with the text prior was significantly better compared to not using a prior, both on a well known microarray data set and on three independent microarray data sets.

A Framework for Elucidating Regulatory Networks Based on Prior Information and Expression Data

Olivier Gevaert — 2007-11-19T21:29:55-00:00

Annals of the New York Academy of Sciences, Vol. 1115, No. 1. (December 2007), pp. 240-248.

Inference in Bayesian networks

Chris Needham — 2006-01-11T03:42:50-00:00

Nature Biotechnology, Vol. 24, No. 1., pp. 51-53.

Bayesian clinical trials

Donald Berry — 2006-01-06T03:32:07-00:00

Nature Reviews Drug Discovery, Vol. 5, No. 1., pp. 27-36.

Bayesian measures of model complexity and fit

SD Spiegelhalter — 2005-05-19T20:35:05-00:00

Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 64, No. 4. (2002), pp. 583-639.

We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher’s information and the posterior covariance, which in normal models is the trace of the ‘hat’ matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding pD to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.

A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).

OG Troyanskaya — 2005-08-30T17:52:41-00:00

Proc Natl Acad Sci U S A, Vol. 100, No. 14. (8 July 2003), pp. 8348-8353.

Genomic sequencing is no longer a novelty, but gene function annotation remains a key challenge in modern biology. A variety of functional genomics experimental techniques are available, from classic methods such as affinity precipitation to advanced high-throughput techniques such as gene expression microarrays. In the future, more disparate methods will be developed, further increasing the need for integrated computational analysis of data generated by these studies. We address this problem with MAGIC (Multisource Association of Genes by Integration of Clusters), a general framework that uses formal Bayesian reasoning to integrate heterogeneous types of high-throughput biological data (such as large-scale two-hybrid screens and multiple microarray analyses) for accurate gene function prediction. The system formally incorporates expert knowledge about relative accuracies of data sources to combine them within a normative framework. MAGIC provides a belief level with its output that allows the user to vary the stringency of predictions. We applied MAGIC to Saccharomyces cerevisiae genetic and physical interactions, microarray, and transcription factor binding sites data and assessed the biological relevance of gene groupings using Gene Ontology annotations produced by the Saccharomyces Genome Database. We found that by creating functional groupings based on heterogeneous data types, MAGIC improved accuracy of the groupings compared with microarray analysis alone. We describe several of the biological gene groupings identified.

A tutorial on learning with bayesian networks

D Heckerman — 2005-03-30T13:29:42-00:00

(# 1995)

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to...

THE BAYESIAN REVOLUTION IN GENETICS

Mark Beaumont — 2005-11-04T18:43:48-00:00

Nat Rev Genet, Vol. 5, No. 4. (April 2004), pp. 251-261.