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

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      Author(s)
      Kuru, Halil İbrahim
      Büyüközkan, Mustafa
      Tastan, Öznur
      Date
      2021-05-26
      Source Title
      PL o S Computational Biology
      Print ISSN
      1553-734X
      Publisher
      Public Library of Science
      Volume
      17
      Issue
      5
      Pages
      1 - 20
      Language
      English
      Type
      Article
      Item Usage Stats
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      49
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      Abstract
      Changes 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.
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      http://hdl.handle.net/11693/77453
      Published Version (Please cite this version)
      https://doi.org/10.1371/journal.pcbi.1008998
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      • Department of Computer Engineering 1561
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