ArticleScientific reports2022
PHEVIR: an artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Leveraging multimodal cancer immunotherapy to amplify the efficacy of oncolytic viruses.Experimental hematology & oncology · 2026Review
- Exploring the potential link between human papillomavirus infection and coronary artery disease: a review of shared pathways and mechanisms.Molecular and cellular biochemistry · 2025Review
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
Infectious diseases are known to cause a wide variety of post-infection complications. However, it's been challenging to identify which diseases are most associated with a given pathogen infection. Using the recently developed LeMeDISCO approach that predicts comorbid diseases associated with a given set of putative mode of action (MOA) proteins and pathogen-human protein interactomes, we developed PHEVIR, an algorithm which predicts the corresponding human disease comorbidities of 312 viruses and 57 bacteria. These predictions provide an understanding of the molecular bases of complications and means of identifying appropriate drug targets to treat them. As an illustration of its power, PHEVIR is applied to identify putative driver pathogens and corresponding human MOA proteins for Type 2 diabetes, atherosclerosis, Alzheimer's disease, and inflammatory bowel disease. Additionally, we explore the origins of the oncogenicity/oncolyticity of certain pathogens and the relationship between heart disease and influenza. The full PHEVIR database is available at https://sites.gatech.edu/cssb/phevir/ .
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