PRER: a patient representation with pairwise relative expression of proteins on biological networks

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buir.contributor.authorKuru, Halil İbrahim
buir.contributor.authorBüyüközkan, Mustafa
buir.contributor.orcidKuru, Halil İbrahim|0000-0003-4356-8846
buir.contributor.orcidBüyüközkan, Mustafa|0000-0001-5027-5078
dc.citation.epage20en_US
dc.citation.issueNumber5en_US
dc.citation.spage1en_US
dc.citation.volumeNumber17en_US
dc.contributor.authorKuru, Halil İbrahim
dc.contributor.authorBüyüközkan, Mustafa
dc.contributor.authorTastan, Öznur
dc.date.accessioned2022-02-17T08:16:22Z
dc.date.available2022-02-17T08:16:22Z
dc.date.issued2021-05-26
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractChanges in protein and gene expression levels are often used as features in predictive modeling such as survival prediction. A common strategy to aggregate information contained in individual proteins is to integrate the expression levels with the biological networks. In this work, we propose a novel patient representation where we integrate proteins’ expression levels with the protein-protein interaction (PPI) networks: Patient representation with PRER (Pairwise Relative Expressions with Random walks). PRER captures the dysregulation patterns of proteins based on the neighborhood of a protein in the PPI network. Specifically, PRER computes a feature vector for a patient by comparing the source protein’s expression level with other proteins’ levels that are within its neighborhood. The neighborhood of the source protein is derived by biased random-walk strategy on the network. We test PRER’s performance in survival prediction task in 10 different cancers using random forest survival models. PRER yields a statistically significant predictive performance in 9 out of 10 cancers when compared to the same model trained with features based on individual protein expressions. Furthermore, we identified the pairs of proteins that their interactions are predictive of patient survival but their individual expression levels are not. The set of identified relations provides a valuable collection of protein biomarkers with high prognostic value. PRER can be used for other complex diseases and prediction tasks that use molecular expression profiles as input. PRER is freely available at: https://github.com/hikuru/PRER.en_US
dc.description.provenanceSubmitted by Türkan Cesur (cturkan@bilkent.edu.tr) on 2022-02-17T08:16:22Z No. of bitstreams: 1 PRER_A_patient_representation_with_pairwise_relative_expression_of_proteins_on_biological_networks.pdf: 2444000 bytes, checksum: 8f2f68bef68799813b7a184c143cc81d (MD5)en
dc.description.provenanceMade available in DSpace on 2022-02-17T08:16:22Z (GMT). No. of bitstreams: 1 PRER_A_patient_representation_with_pairwise_relative_expression_of_proteins_on_biological_networks.pdf: 2444000 bytes, checksum: 8f2f68bef68799813b7a184c143cc81d (MD5) Previous issue date: 2021-05-26en
dc.identifier.doi10.1371/journal.pcbi.1008998en_US
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/11693/77453
dc.language.isoEnglishen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttps://doi.org/10.1371/journal.pcbi.1008998en_US
dc.source.titlePL o S Computational Biologyen_US
dc.titlePRER: a patient representation with pairwise relative expression of proteins on biological networksen_US
dc.typeArticleen_US

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