Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction.

Authors

  • Nansu Zong
    Health System Department of Biomedical Informatics, University of California, San Diego, La Jolla, California, USA.
  • Hyeoneui Kim
    Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA 92093, USA.
  • Victoria Ngo
    Betty Irene Moore School of Nursing, UC Davis, Sacramento, CA 95817, USA.
  • Olivier Harismendy
    Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA 92093, USA.