ArticlePLoS computational biology2024
Prediction of virus-host associations using protein language models and multiple instance learning.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From genomic signals to prediction tools: a critical feature analysis and rigorous benchmark for phage-host prediction.Briefings in bioinformatics · 2025Pooled it
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- GiantHost: a domain-adaptive and uncertainty-aware framework for giant virus host prediction.Bioinformatics (Oxford, England) · 2026Article
- Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.Scientific reports · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Review
- An improved dataset for predicting mammal infecting viruses from genetic sequence information.PLoS computational biology · 2026Article
- Protein language models enable accurate viral host range prediction.Scientific reports · 2026Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- Protein Set Transformer: a protein-based genome language model to power high-diversity viromics.Nature communications · 2025Article
- PLM-interact: extending protein language models to predict protein-protein interactions.Nature communications · 2025Article
- Large language models for biological sequence analysis in infectious disease research.Biosafety and health · 2025Review
- The effect of taxonomic, host-dependent features and sample bias on virus host prediction using machine learning and short sequence k-mers.Scientific reports · 2025Article
- Hidden challenges in evaluating spillover risk of zoonotic viruses using machine learning models.Communications medicine · 2025Article
- Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality.Biomolecules · 2024Article
- Recent advances in deep learning and language models for studying the microbiome.Frontiers in genetics · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Predicting virus-host associations is essential to determine the specific host species that viruses interact with, and discover if new viruses infect humans and animals. Currently, the host of the majority of viruses is unknown, particularly in microbiomes. To address this challenge, we introduce EvoMIL, a deep learning method that predicts the host species for viruses from viral sequences only. It also identifies important viral proteins that significantly contribute to host prediction. The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than sequence composition features, including amino acids, physiochemical properties, and DNA k-mers. In multi-host prediction tasks, EvoMIL achieves median F1 score improvements of 10.8%, 16.2%, and 4.9% in prokaryotic hosts, and 1.7%, 6.6% and 11.5% in eukaryotic hosts. EvoMIL binary classifiers achieve impressive AUC over 0.95 for all prokaryotic hosts and range from roughly 0.8 to 0.9 for eukaryotic hosts. Furthermore, EvoMIL identifies important proteins in the prediction task, capturing key functions involved in virus-host specificity.
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Registered trials
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.