ArticlePLoS computational biology2020
PhANNs, a fast and accurate tool and web server to classify phage structural proteins.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 2 of them syntheses that pooled it.
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Who cites it
69 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Phage bioinformatics tools: a review of computational approaches for bacteriophage research.Briefings in bioinformatics · 2026Review
- EAGP: an efficient generative augmentation framework for phage protein classification under severe class imbalance.Bioinformatics (Oxford, England) · 2026Article
- Phage Frontiers: Genomic and Functional Profiling of Novel Virulent Agents Targeting FoodborneBiology · 2026Article
- From structure to design: experimental and AI-driven approaches in receptor-binding protein engineering for reprogramming phage host range.Archives of microbiology · 2026Review
- The epidermal ecotone: a proposed model system for marine viral ecology at the animal-environmental interface.NPJ biofilms and microbiomes · 2026Review
- Protein structure-informed bacteriophage genome annotation with Phold.Nucleic acids research · 2026Article
- Artificial Intelligence Applications in Bacteriophage Genomics and Host Interaction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- StackPVP: a stacked ensemble classification framework for predicting phage virion proteins using integrated evolutionary features.Frontiers in microbiology · 2026Article
- Characterization and genomic insights into bacteriophages Kpph1 and Kpph9 against hypervirulent carbapenem-resistantVirulence · 2025Article
- Empathi: embedding-based phage protein annotation tool by hierarchical assignment.Nature communications · 2025Article
- Computational function prediction of bacteria and phage proteins.Microbiology and molecular biology reviews : MMBR · 2025Review
- Phage quest: a beginner's guide to explore viral diversity in the prokaryotic world.Briefings in bioinformatics · 2025Review
- Systematic, high-throughput characterization of bacteriophage gene essentiality on diverse hosts.Cell host & microbe · 2025Article
- Exo-Tox: Identifying Exotoxins from secreted bacterial proteins.BioData mining · 2025Article
- Fold first, ask later: structure-informed function annotation ofbioRxiv : the preprint server for biology · 2025Article
- RBPseg: Toward a complete phage tail fiber structure atlas.Science advances · 2025Article
- ProtPhage: a deep learning framework for phage viral protein identification and functional annotation.Briefings in bioinformatics · 2025Article
9 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
For any given bacteriophage genome or phage-derived sequences in metagenomic data sets, we are unable to assign a function to 50-90% of genes, or more. Structural protein-encoding genes constitute a large fraction of the average phage genome and are among the most divergent and difficult-to-identify genes using homology-based methods. To understand the functions encoded by phages, their contributions to their environments, and to help gauge their utility as potential phage therapy agents, we have developed a new approach to classify phage ORFs into ten major classes of structural proteins or into an "other" category. The resulting tool is named PhANNs (Phage Artificial Neural Networks). We built a database of 538,213 manually curated phage protein sequences that we split into eleven subsets (10 for cross-validation, one for testing) using a novel clustering method that ensures there are no homologous proteins between sets yet maintains the maximum sequence diversity for training. An Artificial Neural Network ensemble trained on features extracted from those sets reached a test F1-score of 0.875 and test accuracy of 86.2%. PhANNs can rapidly classify proteins into one of the ten structural classes or, if not predicted to fall in one of the ten classes, as "other," providing a new approach for functional annotation of phage proteins. PhANNs is open source and can be run from our web server or installed locally.
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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.