Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae.

ABSTRACT: The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN). For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these problems, we introduce a Boolean Function Network (BFN) model based on techniques of hidden Markov model (HMM), likelihood ratio test and Boolean logic functions. BFN consists of two consecutive tests to establish links between pairs of genes and check their directness. We evaluate the performance of BFN through the application to S. cerevisiae time course data. BFN produces regulatory relations which s

SUBMITTER: Simak M 

PROVIDER: S-EPMC5628832 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature