title: A multiorganism based method for Bayesian gene network estimation.
abstract: The primary goal of this article is to infer genetic interactions based on gene expression data. A new method for multiorganism Bayesian gene network estimation is presented based on multitask learning. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from true correlations that would lead to actual edges when modeling the gene interactions as a Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes.
authors.affiliation: Department of Electrical and Computer Engineering, American University of Beirut, Riad El-Solh, Lebanon. zd03@aub.edu.lb
authors.forename: Zaher
authors.initials: Z
authors.lastname: Dawy
authors.forename: Elias
authors.initials: E
authors.lastname: Yaacoub
authors.forename: Marcel
authors.initials: M
authors.lastname: Nassar
authors.forename: Rami
authors.initials: R
authors.lastname: Abdallah
authors.forename: Hady Ali
authors.initials: HA
authors.lastname: Zeineddine
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015870:Gene Expression; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D017385:Sequence Homology