Multi-modal data integration to identify kinase substrates

Gaurav Pandey, Ph.D.
Associate Professor
Departments of Genetics and Genomic Sciences and AI
and Human Health Icahn School of Medicine at Mount Sinai


Avner Schlessinger, Ph.D.
Associate Professor
Pharmacological Sciences Icahn School of Medicine at Mount Sinai


Lab websites:

  • Gaurav Pendey profile: https://icahn.mssm.edu/profiles/gaurav-pandey
  • Dr. Pendey's Lab Website: https://gpandeylab.org/
  • Avner Schlessinger profile: https://icahn.mssm.edu/profiles/avner-schlessinger/
  • Dr. Schlessinger's Lab Website: https://www.schlessingerlab.org/


Despite the importance of protein kinases for human biology and drug discovery, there is a gap in the knowledge of their substrates, and thus functions, of these critical proteins. To address this challenge, we will develop a novel computational framework to predict kinase-substrate interactions by combining biologically relevant multi-modal data sources with cutting-edge machine learning methodologies. This work has the potential to significantly advance our ability to infer substrates and functions of human kinases, especially the understudied members of this biomedically important protein family, thereby contributing to the illumination of the druggable genome.

NIH grant number: U01 CA271318



  1. Stein D, Kars ME, Wu Y, Bayrak CS, Stenson PD, Cooper DN, Schlessinger A, Itan Y. Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set. Genome Med. 2023 Nov 30;15(1):103. doi: 10.1186/s13073-023-01261-9. PMID: 38037155, PMCID: PMC10688473.

Page reviewed on March 8, 2024