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Reconstructing transcriptional regulatory networks by probabilistic network component analysis

Authors:

Ayesha N. Shajahan,

Leena Hilakivi-Clarke,

Robert ClarkeAuthors Info & Claims

BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

Pages 96 - 105

https://doi.org/10.1145/2506583.2506599

Published: 22 September 2013 Publication History

Abstract

Despite encouraging progress made by integrating multi-platform data for regulatory network reconstruction, identification of transcriptional regulatory networks remains challenging due to imperfection in current biotechnology and complexity of biological systems. It is important to develop new computational approaches for reliable regulatory network reconstruction, especially those of robustness against noise in gene expression data and 'structural error' (i.e., false connections) in binding data. We propose a new method, namely probabilistic network component analysis (pNCA), to estimate the posterior binding matrix given observed gene expression and binding data. The elements in the binding matrix, instead of taking deterministic binary values, are modeled as unknown Bernoulli random variables that represent the probability of regulation. A novel two-stage Gibbs sampling framework is employed to iteratively estimate both hidden transcription factor activities and the posterior distribution of binding matrix. Numerical simulation on synthetic data has demonstrated improved performance of the proposed method over several existing methods for regulatory network identification. Notably, the robustness of pNCA against 'structural error' in initial binding data is fortified with high tolerance of false negative connections in addition to that of false positive connections. The proposed method has been applied to breast cancer cell line data to reconstruct biologically meaningful regulatory networks, revealing condition-specific regulatory rewiring and important cooperative regulation associated with estrogen signaling and action in breast cancer cells.

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Cited By

Arrieta‐Ortiz MHafemeister CBate AChu TGreenfield AShuster BBarry SGallitto MLiu BKacmarczyk TSantoriello FChen JRodrigues CSato TRudner DDriks ABonneau REichenberger P(2015) An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network Molecular Systems Biology10.15252/msb.2015623611:11Online publication date: 17-Nov-2015
https://doi.org/10.15252/msb.20156236

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Reconstructing transcriptional regulatory networks by probabilistic network component analysis

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cover image ACM Conferences

BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

September 2013

987 pages

ISBN:9781450324342

DOI:10.1145/2506583

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 September 2013

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BCB'13: ACM-BCB2013

September 22 - 25, 2013

Wshington DC, USA

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BCB'13 Paper Acceptance Rate 43 of 148 submissions, 29%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Arrieta‐Ortiz MHafemeister CBate AChu TGreenfield AShuster BBarry SGallitto MLiu BKacmarczyk TSantoriello FChen JRodrigues CSato TRudner DDriks ABonneau REichenberger P(2015) An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network Molecular Systems Biology10.15252/msb.2015623611:11Online publication date: 17-Nov-2015
https://doi.org/10.15252/msb.20156236

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