A Prioritization Method for Identifying Disease-Causative Gene Based on Hyper Graph Network

Author(s): Haoyue Fu, Lianping Yang, Xiangde Zhang

Journal Name: Current Proteomics

Volume 13 , Issue 2 , 2016

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Abstract:

Backgrounds: Difficulty on identification of transcription factor binding site lies in, compared with those hundreds or thousands bp background noise sequences, the motif signals with ten to several tens bp in length are rather short; moreover, the motif instance of a transcription factor is likely to mutate partially. The TFBS identification has always been a challenge task.

Results: The experimental methods which are widely used in the study on transcription regulation, the databases that collect information on TFBS, the models that represent TFBS and the TFBS identification algorithms are introduced and reviewed systematically in this paper.

Conclusion: The regulation mechanism of TFBS in the regulation network is to be further discovered. We insist that the progress on experiment technology and the insight into the regulation mechanism will definitely bring new life into the bioinformatics on TFBS. Since deep learning method has manifested the excellent performance on identification of TFBS, there are good reasons to believe that integrated more up-to-date biological data, the deep learning method will become the dominant way to study transcription regulation.

Keywords: Protein interaction network, complex disease, hyper graph, dense sub-graph, function module, prioritization method.

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Article Details

VOLUME: 13
ISSUE: 2
Year: 2016
Page: [158 - 163]
Pages: 6
DOI: 10.2174/157016461302160514005851
Price: $25

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