ArticleiScience2026
FunVFPred: Predicting fungal virulence factors using a unified representation learning model.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Fungal virulence factors (VFs) are proteins that support host colonization, tissue invasion, immune modulation, and disease progression. Identifying candidate VFs across pathogenic fungi can help characterize mechanisms underlying fungal-host interactions and prioritize proteins for experimental investigation. We developed FunVFPred, a machine learning framework that uses UniRep protein sequence embeddings, and multiple classifiers to predict candidate fungal VFs. Using experimentally validated VFs from human-pathogenic
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.