Now showing items 1-4 of 4

    • DeepSide: a deep learning approach for drug side effect prediction 

      Üner, Onur Can; Kuru, Halil İbrahim; Cinbiş, R. Gökberk; Taştan, Öznur; Çiçek, A. Erüment (IEEE, 2022-01-07)
      Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug ...
    • Graph embeddings on protein interaction networks 

      Kuru, Halil İbrahim (Bilkent University, 2019-02)
      Protein-protein interaction (PPI) networks represent the possible set of interactions among proteins and thereby the genes that code for them. By integrating isolated signals on single genes such as mutations or differential ...
    • MatchMaker: A deep learning framework for drug synergy prediction 

      Kuru, Halil İbrahim; Taştan, Ö.; Çiçek, Ercüment (IEEE, 2021-06-04)
      Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations ...
    • PRER: a patient representation with pairwise relative expression of proteins on biological networks 

      Kuru, Halil İbrahim; Büyüközkan, Mustafa; Tastan, Öznur (Public Library of Science, 2021-05-26)
      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 ...