Reconstruction of genetic regulatory networks based on the posterior probabilities of gene regulations

Wentao Zhao, Kwadwo Agyepong, Erchin Serpedin, Edward R. Dougherty

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

2 Citations (Scopus)

Abstract

Recent advances in high throughput microarray data have enabled the learning of the structure and operation of gene regulatory networks. This paper proposes a novel approach for reconstruction of gene regulatory networks based on the posterior probabilities of gene regulations. Built within the framework of Bayesian statistics and exploiting efficient computational Monte Carlo techniques, the proposed approach prevents the dichotomy of classifying gene interactions as either being connected or disconnected, and thereby it reduces significantly the inference errors. Simulation results corroborate the superior performance of the proposed approach relative to the existing state-of-the-art algorithms.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Volume1
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: 15 Apr 200720 Apr 2007

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period15/4/0720/4/07

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Keywords

  • Biological systems
  • Genetics
  • Monte Carlo methods

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

Cite this

Zhao, W., Agyepong, K., Serpedin, E., & Dougherty, E. R. (2007). Reconstruction of genetic regulatory networks based on the posterior probabilities of gene regulations. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 (Vol. 1). [4217090] https://doi.org/10.1109/ICASSP.2007.366690