A deep neural network approach for learning intrinsic protein-RNA binding preferences.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: The complexes formed by binding of proteins to RNAs play key roles in many biological processes, such as splicing, gene expression regulation, translation and viral replication. Understanding protein-RNA binding may thus provide important insights to the functionality and dynamics of many cellular processes. This has sparked substantial interest in exploring protein-RNA binding experimentally, and predicting it computationally. The key computational challenge is to efficiently and accurately infer protein-RNA binding models that will enable prediction of novel protein-RNA interactions to additional transcripts of interest.

Authors

  • Ilan Ben-Bassat
    Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.
  • Benny Chor
    Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.
  • Yaron Orenstein
    Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.