Multitask Learning of Signaling and Regulatory Networks with Application to Studying Human Response to Flu
Name
Jain-2014-Multitask Learning o.pdf
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
5010e9b2e7e6c33d3c43abf691079c42
Author(s)
Jain, Siddhartha
Gitter, Anthony
Bar-Joseph, Ziv
Date Issued
December 2014
Journal
PLoS Computational Biology
Publisher
Public Library of Science
Citation
Jain, Siddhartha, Anthony Gitter, and Ziv Bar-Joseph. “Multitask Learning of Signaling and Regulatory Networks with Application to Studying Human Response to Flu.” Edited by Mona Singh. PLoS Comput Biol 10, no. 12 (December 18, 2014): e1003943.
Version
Final published version
Abstract
Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1003943
