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Multiple linear regression for reconstruction of gene regulatory networks in solving cascade error problems

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dc.citedby16
dc.contributor.authorSalleh F.H.M.en_US
dc.contributor.authorZainudin S.en_US
dc.contributor.authorArif S.M.en_US
dc.contributor.authorid26423229000en_US
dc.contributor.authorid24479069300en_US
dc.contributor.authorid26646287700en_US
dc.date.accessioned2023-05-29T06:40:50Z
dc.date.available2023-05-29T06:40:50Z
dc.date.issued2017
dc.description.abstractGene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction ismisinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A ? B ? C) as a direct interaction (A ? C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. Copyright � 2017 Faridah Hani Mohamed Salleh et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4827171
dc.identifier.doi10.1155/2017/4827171
dc.identifier.scopus2-s2.0-85013276084
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85013276084&doi=10.1155%2f2017%2f4827171&partnerID=40&md5=8b92fa890aac007ce5f8b936630f1050
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23476
dc.identifier.volume2017
dc.publisherHindawi Publishing Corporationen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleAdvances in Bioinformatics
dc.titleMultiple linear regression for reconstruction of gene regulatory networks in solving cascade error problemsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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