title: Reconstructing Causal Biological Networks through Active Learning.
abstract: Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States of America.
authors.forename: Hyunghoon
authors.initials: H
authors.lastname: Cho
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States of America.|Department of Mathematics, MIT, Cambridge, MA, United States of America.
authors.forename: Bonnie
authors.initials: B
authors.lastname: Berger
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States of America.|Department of Mathematics, MIT, Cambridge, MA, United States of America.|Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States of America.
authors.forename: Jian
authors.initials: J
authors.lastname: Peng
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D053263:Gene Regulatory Networks; D006801:Humans; D000069550:Machine Learning; D008954:Models, Biological