NDLI: Exploiting Binary Abstractions in Deciphering Gene Interactions

Content Provider	IEEE Xplore Digital Library
Author	Yoon, S. Garg, A. Eui-Young Chung Hyun Seok Park Woong Yang Park De Micheli, D.
Copyright Year	2006
Description	Author affiliation: Comput. Syst. Lab., Stanford Univ., CA (Yoon, S.)
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
Sponsorship	IEEE EMB
Starting Page	5858
Ending Page	5863
File Size	263707
Page Count	6
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.260194
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene expression Computer networks Boolean functions DNA Robustness Bayesian methods Bioinformatics Biomedical engineering Biology computing Signal processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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