Inferring the structure of genetic regulatory networks using information theoretic tools

Wentao Zhao, Erchin Serpedin, Edward R. Dougherty

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

1 Citation (Scopus)

Abstract

By combining the mutual information and conditional mutual information, a practical metric is proposed to capture the inference confidence of direct connectivity between two genes. This metric helps to avoid the disadvantage of general schemes, i.e., the dichotomy of either being connected or disconnected. Based on data sets generated by synthetic networks, the performance of proposed algorithm is compared favorably with respect to other schemes in the literature. The proposed algorithm is also applied on realistic cutaneous melanoma data set to recover a genetic network containing 470 genes.

Original languageEnglish
Title of host publication2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 - Bethesda, MD, United States
Duration: 13 Jul 200614 Jul 2006

Other

Other2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
CountryUnited States
CityBethesda, MD
Period13/7/0614/7/06

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ASJC Scopus subject areas

  • Health(social science)
  • Assessment and Diagnosis
  • Medicine(all)
  • Health Information Management
  • Electrical and Electronic Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Signal Processing

Cite this

Zhao, W., Serpedin, E., & Dougherty, E. R. (2006). Inferring the structure of genetic regulatory networks using information theoretic tools. In 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 [4015780] https://doi.org/10.1109/LSSA.2006.250379