title: Temporal graphical models for cross-species gene regulatory network discovery.
abstract: Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causality relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L(1) penalty to uncover the regulatory network structure for different species. The algorithm provides a framework for sharing information across species via hidden component graphs and is able to incorporate domain knowledge across species easily. We demonstrate our method on two synthetic datasets and apply it to discover causal graphs from innate immune response data.
authors.affiliation: Computer Science Department, University of Southern California, 941 Bloom Walk SAL 300, Los Angeles, CA 90089, USA. yanliu.cs@usc.edu
authors.forename: Yan
authors.initials: Y
authors.lastname: Liu
authors.forename: Alexandru
authors.initials: A
authors.lastname: Niculescu-Mizil
authors.forename: Aurélie
authors.initials: A
authors.lastname: Lozano
authors.forename: Yong
authors.initials: Y
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D003196:Computer Graphics; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D006801:Humans; D055261:Immunogenetic Phenomena; D008390:Markov Chains; D051379:Mice; D046228:Microarray Analysis / Q000706:statistics & numerical data; D008957:Models, Genetic; D012044:Regression Analysis; D013045:Species Specificity