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Advances to Bayesian network inference for generating causal networks from observational biological data.

dc.contributor.author Hartemink, Alexander J
dc.contributor.author Jarvis, Erich David
dc.contributor.author Smith, VA
dc.contributor.author Wang, PP
dc.contributor.author Yu, J
dc.coverage.spatial England
dc.date.accessioned 2015-12-19T14:27:51Z
dc.date.issued 2004-12-12
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/15284094
dc.identifier bth448
dc.identifier.issn 1367-4803
dc.identifier.uri http://hdl.handle.net/10161/11228
dc.description.abstract MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/
dc.language eng
dc.relation.ispartof Bioinformatics
dc.relation.isversionof 10.1093/bioinformatics/bth448
dc.subject Algorithms
dc.subject Bayes Theorem
dc.subject Computer Simulation
dc.subject Gene Expression Profiling
dc.subject Gene Expression Regulation
dc.subject Models, Genetic
dc.subject Models, Statistical
dc.subject Oligonucleotide Array Sequence Analysis
dc.subject Signal Transduction
dc.subject Software
dc.title Advances to Bayesian network inference for generating causal networks from observational biological data.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/15284094
pubs.begin-page 3594
pubs.end-page 3603
pubs.issue 18
pubs.organisational-group Basic Science Departments
pubs.organisational-group Computer Science
pubs.organisational-group Duke
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Neurobiology
pubs.organisational-group School of Medicine
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 20


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