Learning a Probabilistic Boolean Network model from biological pathways and time-series expression data

The problem of inferring a stochastic model for gene regulatory networks is addressed here. The prior biological data includes biological pathways and time-series expression data. We propose a novel algorithm to use both of these data to construct a Probabilistic Boolean Network (PBN) which models t...

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Bibliographic Details
Published in2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2016; pp. 1471 - 1475
Main Authors Pahuja, Vardaan, Layek, Ritwik Kumar, Mitra, Pabitra
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.08.2016
Subjects
Algorithms
Biological system modeling
Boolean functions
Data models
Gene Regulatory Networks
Heuristic algorithms
Mathematical model
Models, Genetic
Models, Statistical
Probabilistic logic
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Summary:The problem of inferring a stochastic model for gene regulatory networks is addressed here. The prior biological data includes biological pathways and time-series expression data. We propose a novel algorithm to use both of these data to construct a Probabilistic Boolean Network (PBN) which models the observed dynamics of genes with a high degree of precision. Our algorithm constructs a pathway tree and uses the time-series expression data to select an optimal level of tree, whose nodes are used to infer the PBN.
ISSN:1557-170X
DOI:10.1109/EMBC.2016.7590987