Predicting host-pathogen interactions with machine learning algorithms: A scoping review.

Journal: Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
PMID: 40220943

Abstract

BACKGROUND: Diseases caused by pathogenic microorganisms pose a persistent global health challenge. Pathogens exploit host mechanisms through intricate molecular interactions. Understanding these host-pathogen interactions (HPIs), particularly protein-protein interactions (PPIs), is crucial for developing therapeutic strategies. While experimental approaches are essential, they are often labor-intensive and costly. Researchers have been able to predict HPIs more efficiently due to recent advances in artificial intelligence and machine learning. However, existing reviews lack a systematic evaluation of different machine learning methodologies and their effectiveness.

Authors

  • Rasool Sahragard
    Molecular Biology Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Masoud Arabfard
    Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran.
  • Ali Najafi
    Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA. vsiyer@uw.edu gshyam@uw.edu.

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