Inference of gene-regulatory networks using message-passing algorithms

Manohar Shamaiah, Sang Hyun Lee, Haris Vikalo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present an application of message-passing techniques to gene regulatory network inference. The network inference is posed as a constrained linear regression problem, and solved by a distributed computationally efficient message-passing algorithm. Performance of the proposed algorithm is tested on gold standard data sets and evaluated using metrics provided by the DREAM2 challenge [1]Performance of the proposed algorithm is comparable to that of the techniques which yielded the best results in the DREAM2 challenge competition.

Original languageEnglish
Title of host publication2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
DOIs
Publication statusPublished - 2010 Dec 1
Externally publishedYes
Event2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010 - Cold Spring Harbor, NY, United States
Duration: 2010 Nov 102010 Nov 12

Publication series

Name2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010

Conference

Conference2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
CountryUnited States
CityCold Spring Harbor, NY
Period10/11/1010/11/12

Keywords

  • Gene regulatory networks
  • L1-regularized model
  • Message passing algorithms

ASJC Scopus subject areas

  • Genetics
  • Signal Processing

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  • Cite this

    Shamaiah, M., Lee, S. H., & Vikalo, H. (2010). Inference of gene-regulatory networks using message-passing algorithms. In 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010 [5719683] (2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010). https://doi.org/10.1109/GENSIPS.2010.5719683