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多示例学习识别序列Motif

Jin Gu. A multiple-instance scoring method to predict tissue-specific cis-regulatory motifs and regions. Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS'10) 2010, Vol I:186-190. (Oral Presentation)

BMC Bioinformatics

Proceedings

BioMed Central

Open Access

Motif-directed network component analysis for regulatory network

inference

ChenWang1, JianhuaXuan*1, LiChen1, PoZhao2, YueWang1, RobertClarke3 and EricHoffman2

Address: 1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA, 2Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC, USA and 3Departments of Oncology and Physiology & Biophysics, Georgetown University School of Medicine, Washington, DC, USA

Email: ChenWang-topsoil@vt.edu; JianhuaXuan*-xuan@vt.edu; LiChen-lchen06@vt.edu; PoZhao-pzhao@http://www.wenkuxiazai.com; YueWang-yuewang@vt.edu; RobertClarke-clarker@georgetown.edu; EricHoffman-ehoffman@http://www.wenkuxiazai.com* Corresponding author

from Sixth International Conference on Bioinformatics (InCoB2007)Hong Kong. 27–30 August 2007Published: 13 February 2008

BMC Bioinformatics 2008, 9(Suppl 1):S21

doi:10.1186/1471-2105-9-S1-S21

This article is available from: http://www.wenkuxiazai.com/1471-2105/9/S1/S21

© 2008 Wang et al; licensee BioMed Central Ltd.

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: Network Component Analysis (NCA) has shown its effectiveness in discoveringregulators and inferring transcription factor activities (TFAs) when both microarray data and ChIP-on-chip data are available. However, a NCA scheme is not applicable to many biological studies dueto limited topology information available, such as lack of ChIP-on-chip data. We propose a newapproach, motif-directed NCA (mNCA), to integrate motif information and gene expression datato infer regulatory networks.

Results: We develop motif-directed NCA (mNCA) to incorporate motif information into NCAfor regulatory network inference. While motif information is readily available from knowledgedatabases, it is a "noisy" source of network topology information consisting of many false positives.To overcome this problem, we develop a stability analysis procedure embedded in mNCA toresolve the inconsistency between motif information and gene expression data, and to enable theidentification of stable TFAs. The mNCA approach has been applied to a time course microarraydata set of muscle regeneration. The experimental results show that the inferred TFAs are not onlynumerically stable but also biologically relevant to muscle differentiation process. In particular,several inferred TFAs like those of MyoD, myogenin and YY1 are well supported by biologicalexperiments.

Conclusion: A novel computational approach, mNCA, has been developed to integrate motifinformation and gene expression data for regulatory network reconstruction. Specifically, motifanalysis is used to obtain initial network topology, and stability analysis is developed and appliedwith mNCA to extract stable TFAs. Experimental results on muscle regeneration microarray datahave demonstrated that mNCA is a practical and reliable computational method for regulatorynetwork inference and pathway discovery.

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