Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning.

Journal: Journal of translational medicine
Published Date:

Abstract

BACKGROUND: Biological systems derive from complex interactions between entities ranging from biomolecules to macroscopic structures, forming intricate networks essential for understanding disease mechanisms and developing therapeutic interventions. Current AI-driven interaction predictors typically operate in isolation, focusing on single tasks and missing the broader picture of how different biological interactions influence each other. Traditional wet-lab approaches for identifying these interactions are expensive, time-consuming, and error-prone. No unified platform currently exists where biologists can predict and analyze multiple types of biological relationships comprehensively, limiting our ability to discover new therapeutic applications and fully understand interconnected biological mechanisms.

Authors

  • Naafey Aamer
    Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany. naafey.aamer@dfki.de.
  • Muhammad Nabeel Asim
    Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan; German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany. Electronic address: nabeel.asim@kics.edu.pk.
  • Aamer Iqbal Bhatti
    Control and Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
  • Andreas Dengel
    German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germnay. Andreas.Dengel@dfki.de.