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Article: A Multi-Laplacian Prior and Augmented Lagrangian Approach to the Exploratory Analysis of Time-Varying Gene and Transcriptional Regulatory Networks for Gene Microarray Data

TitleA Multi-Laplacian Prior and Augmented Lagrangian Approach to the Exploratory Analysis of Time-Varying Gene and Transcriptional Regulatory Networks for Gene Microarray Data
Authors
KeywordsGene regulatory networks
gene microarray
hub gene
time-course data
transcript factor
Issue Date2019
PublisherIEEE.
Citation
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, v. 16 n. 6, p. 1816-1829 How to Cite?
AbstractThis paper proposes a novel multi-Laplacian prior (MLP) and augmented Lagrangian method (ALM) approach for gene interactions and putative transcription factors (TFs) identification from time-course gene microarray data. It employs a non-linear time-varying auto-regressive (N-TVAR) model and the Maximum-A-Posteriori-Probability method for incorporating the multi-Laplacian prior and the continuity constraint. The MLP allows connections to/from a gene to be better preserved for putative TF identification in non-stationarity gene regulatory network as compared with conventional L 1 -based penalties. Moreover, the ALM allows the resultant non-smooth L 1 -based penalties to be decoupled from the remaining smooth terms, so that the former and latter can be efficiently solved using a low-complexity proximity operator and smooth optimization technique, respectively. Synthetic and real time-course gene microarray datasets are tested to evaluate the performance of the proposed method. Experimental results show that the proposed method gives better accuracy and higher computational speed than our previous work using smoothed approximation. Moreover, its performance, without the use of ChIP-chip data, is found to be highly comparable with other state-of-the-art methods integrating both ChIP-chip and gene microarray data. It suggests that the proposed method may serve as a useful exploratory tool for putative TF identification with reduced experimental cost.
Persistent Identifierhttp://hdl.handle.net/10722/293812
ISSN
2023 Impact Factor: 3.6
2023 SCImago Journal Rankings: 0.794
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, L-
dc.contributor.authorWu, HC-
dc.contributor.authorHO, CH-
dc.contributor.authorChan, SC-
dc.date.accessioned2020-11-23T08:22:08Z-
dc.date.available2020-11-23T08:22:08Z-
dc.date.issued2019-
dc.identifier.citationIEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, v. 16 n. 6, p. 1816-1829-
dc.identifier.issn1545-5963-
dc.identifier.urihttp://hdl.handle.net/10722/293812-
dc.description.abstractThis paper proposes a novel multi-Laplacian prior (MLP) and augmented Lagrangian method (ALM) approach for gene interactions and putative transcription factors (TFs) identification from time-course gene microarray data. It employs a non-linear time-varying auto-regressive (N-TVAR) model and the Maximum-A-Posteriori-Probability method for incorporating the multi-Laplacian prior and the continuity constraint. The MLP allows connections to/from a gene to be better preserved for putative TF identification in non-stationarity gene regulatory network as compared with conventional L 1 -based penalties. Moreover, the ALM allows the resultant non-smooth L 1 -based penalties to be decoupled from the remaining smooth terms, so that the former and latter can be efficiently solved using a low-complexity proximity operator and smooth optimization technique, respectively. Synthetic and real time-course gene microarray datasets are tested to evaluate the performance of the proposed method. Experimental results show that the proposed method gives better accuracy and higher computational speed than our previous work using smoothed approximation. Moreover, its performance, without the use of ChIP-chip data, is found to be highly comparable with other state-of-the-art methods integrating both ChIP-chip and gene microarray data. It suggests that the proposed method may serve as a useful exploratory tool for putative TF identification with reduced experimental cost.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE/ACM Transactions on Computational Biology and Bioinformatics-
dc.rightsIEEE/ACM Transactions on Computational Biology and Bioinformatics. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectGene regulatory networks-
dc.subjectgene microarray-
dc.subjecthub gene-
dc.subjecttime-course data-
dc.subjecttranscript factor-
dc.titleA Multi-Laplacian Prior and Augmented Lagrangian Approach to the Exploratory Analysis of Time-Varying Gene and Transcriptional Regulatory Networks for Gene Microarray Data-
dc.typeArticle-
dc.identifier.emailWu, HC: hcwueee@hku.hk-
dc.identifier.emailChan, SC: scchan@eee.hku.hk-
dc.identifier.authorityChan, SC=rp00094-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TCBB.2018.2828810-
dc.identifier.pmid29993914-
dc.identifier.scopuseid_2-s2.0-85059046777-
dc.identifier.hkuros319242-
dc.identifier.volume16-
dc.identifier.issue6-
dc.identifier.spage1816-
dc.identifier.epage1829-
dc.identifier.isiWOS:000507924300005-
dc.publisher.placeUnited States-
dc.identifier.issnl1545-5963-

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