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Reconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Models

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dc.citedby2
dc.contributor.authorMohamed Salleh F.H.en_US
dc.contributor.authorZainudin S.en_US
dc.contributor.authorRaih M.F.en_US
dc.contributor.authorid26423229000en_US
dc.contributor.authorid24479069300en_US
dc.contributor.authorid57221461047en_US
dc.date.accessioned2023-05-29T07:27:26Z
dc.date.available2023-05-29T07:27:26Z
dc.date.issued2019
dc.descriptionBig data; Complex networks; Diseases; Escherichia coli; Gene expression; Least squares approximations; Multivariant analysis; Regression analysis; Computational analysis; Gene regulatory networks; Large-scale gene regulatory networks; Multi variate analysis; Partial least square (PLS); PCA (principal component analysis); Regression-based model; Regulatory interactions; Principal component analysisen_US
dc.description.abstractGene regulatory networks (GRN) reconstruction is the process of identifying gene regulatory interactions from experimental data through computational analysis. GRN reconstruction-related works have boosted many major discoveries in finding drug targets for the treatment of human diseases, including cancer. However, reconstructing GRNs from gene expression data is a challenging problem due to high-dimensionality and very limited number of observations data, severe multicollinearity and the tendency of generating cascade errors. These problems lead to the reduced performance of GRN inference methods, hence resulting in the method being unreliable for scientific usage. We propose a method called P-CALS (Principal Component Analysis and Partial Least Squares) that is derived from the combination of PCA (Principal Component Analysis) with PLS (Partial Least Squares). The performance of P-CALS is assessed to the genome-scale GRN of E. coli, S. cerevisiae and an in-silico datasets. We discovered that P-CALS achieved satisfactory results as all of the sub-networks from diverse datasets achieved AUROC values above 0.5 and gene relationships were discovered at the most complex network tested in the experiments. � 2018 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8629777
dc.identifier.doi10.1109/ICBDAA.2018.8629777
dc.identifier.epage134
dc.identifier.scopus2-s2.0-85062777322
dc.identifier.spage129
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062777322&doi=10.1109%2fICBDAA.2018.8629777&partnerID=40&md5=ccefc53ffe564fd4e23590320ef6c794
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24815
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
dc.titleReconstruction of Large-Scale Gene Regulatory Networks Using Regression-based Modelsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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