Reconstructing Causal Biological Networks through Active Learning
Name
Cho-2016-Reconstructing Causa.pdf
Size
657.18 KB
Format
Adobe PDF
Checksum (MD5)
39340ebe508e36747fb07e4972308705
Author(s)
Cho, Hyunghoon
Berger Leighton, Bonnie
Peng, Jian
Date Issued
March 2016
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Cho, Hyunghoon, Bonnie Berger, and Jian Peng. "Reconstructing Causal Biological Networks through Active Learning." PLOS One (March 1, 2016). pp.1-15.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0150611
