XGDAG: explainable gene-disease associations via graph neural networks.

Journal: Bioinformatics (Oxford, England)
Published Date: Aug 1, 2023

Abstract

MOTIVATION: Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene-disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability.

Authors

  • Andrea Mastropietro
    Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG) Sapienza University of Rome, 00185 Rome, Italy; Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany. Electronic address: mastropietro@diag.uniroma1.it.
  • Gianluca De Carlo
    Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome 00185, Italy.
  • Aris Anagnostopoulos
    Department of Computer, Control and Management Engineering (DIAG), Sapienza University of Rome, Rome, Italy.

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