Exploiting binary abstractions in deciphering gene interactions

Sungroh Yoon, Abhishek Garg, Eui Young Chung, Hyun Seok Park, Woong Yang Park, Giovanni De Micheli

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

Abstract

We consider computationally reconstructing gene regulatory networks on top of the binary abstraction of gene expression state information. Unlike previous Boolean network approaches, the proposed method does not handle noisy gene expression values directly. Instead, two-valued "hidden state" information is derived from gene expression profiles using a robust statistical technique, and a gene interaction network is inferred from this hidden state information. In particular, we exploit Espresso, a well-known 2-level Boolean logic optimizer in order to determine the core network structure. The resulting gene interaction networks can be viewed as dynamic Bayesian networks, which have key advantages over more conventional Bayesian networks in terms of biological phenomena that can be represented. The authors tested the proposed method with a time-course gene expression data set from microarray experiments on anti-cancer drugs doxorubicin and paclitaxel. A gene interaction network was produced by our method, and the identified genes were validated with a public annotation database. The experimental studies we conducted suggest that the proposed method inspired by engineering systems can be a very effective tool to decipher complex gene interactions in living systems.

Original languageEnglish
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages5858-5863
Number of pages6
DOIs
Publication statusPublished - 2006
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: 2006 Aug 302006 Sep 3

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Other

Other28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
CountryUnited States
CityNew York, NY
Period06/8/3006/9/3

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Yoon, S., Garg, A., Chung, E. Y., Park, H. S., Park, W. Y., & De Micheli, G. (2006). Exploiting binary abstractions in deciphering gene interactions. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 (pp. 5858-5863). [4030343] (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). https://doi.org/10.1109/IEMBS.2006.260194