NDLI: Identification of Transcriptional Regulatory Networks by Learning the Marginal Function of Outlier Sum Statistic

Content Provider	IEEE Xplore Digital Library
Author	Jinghua Gu Jianhua Xuan Yue Wang Riggins, R.B. Clarke, R.
Copyright Year	2010
Abstract	Network component analysis (NCA) and other methods based on the NCA model have become powerful bioinformatics tools to reconstruct underlying regulatory networks and recover hidden biological processes. However, due to the existence of experimental noises in micro array data and false information in network connectivity data (e.g., ChIP-on-chip binding data, motif information, etc.), it still remains challenging to reconstruct gene regulatory networks for real biomedical applications such as human cancer studies. In this paper, we model the relationship between the genes that share the same transcription factors (TF) from the angle of regression. We propose a statistic called outlier sum testing the conditional significance of the target genes. A Gibbs strategy is utilized in order to estimate the marginal value of outlier sum from its conditional function. Based on the outlier sum statistic we are able to extract the true target genes that carry information about transcription factor activities (TFAs) from the whole population. As a proof-of-concept, we demonstrated the efficiency and robustness of the proposed method on both simulation data and yeast cell cycle data.
Starting Page	281
Ending Page	286
File Size	431157
Page Count	6
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.48
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	network component analysis Biological system modeling Sampling methods transcriptional regulatory network Data models outlier sum Mathematical model Gene expression Gibbs sampling Equations Testing
Content Type	Text
Resource Type	Article

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