ArticleBMC genomics2007
Predicting protein function by machine learning on amino acid sequences--a critical evaluation.
Article in BMC genomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Exploring antibiotic resistance in diverse homologs of the dihydrofolate reductase protein family through broad mutational scanning.Science advances · 2025Article
- Precision enzyme discovery through targeted mining of metagenomic data.Natural products and bioprospecting · 2024Review
- Multimodal pretraining for unsupervised protein representation learning.Biology methods & protocols · 2024Article
- Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences.Journal of the mechanical behavior of biomedical materials · 2022Article
- Functional characterization of prokaryotic dark matter: the road so far and what lies ahead.Current research in microbial sciences · 2022Review
- Machine learning predicts new anti-CRISPR proteins.Nucleic acids research · 2020Article
- Sonification basedAPL bioengineering · 2020Article
- Comparative analysis of prokaryotic and eukaryotic transcription factors using machine-learning techniques.Bioinformation · 2018Article
- Adenine Enrichment at the Fourth CDS Residue in Bacterial Genes Is Consistent with Error Proofing for +1 Frameshifts.Molecular biology and evolution · 2017Article
- DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.Nucleic acids research · 2015Article
- LocTree2 predicts localization for all domains of life.Bioinformatics (Oxford, England) · 2012Article
- Enzyme classification with peptide programs: a comparative study.BMC bioinformatics · 2009Article
- Structural features and the persistence of acquired proteins.Proteomics · 2008Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are able to discriminate between proteins belonging to different functional classes. Until now, however, it has been unclear if this ability would be transferable to proteins of unknown function, which may show distinct biases compared to experimentally more tractable proteins.
resultsHere we show that proteins with known and unknown function do indeed differ significantly. We then show that proteins from different bacterial species also differ to an even larger and very surprising extent, but that functional classifiers nonetheless generalize successfully across species boundaries. We also show that in the case of highly specialized proteomes classifiers from a different, but more conventional, species may in fact outperform the endogenous species-specific classifier.
conclusionWe conclude that there is very good prospect of successfully predicting the function of yet uncharacterized proteins using machine learning classifiers trained on proteins of known function.
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