ArticleBioinformatics (Oxford, England)2013
Parametric Bayesian priors and better choice of negative examples improve protein function prediction.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 36 citations in OpenAlex.
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations.PloS one · 2026Article
- Predicting protein functions using positive-unlabeled ranking with ontology-based priors.Bioinformatics (Oxford, England) · 2024Article
- Computational Methods for Prediction of Human Protein-Phenotype Associations: A Review.Phenomics (Cham, Switzerland) · 2021Review
- Supervised learning is an accurate method for network-based gene classification.Bioinformatics (Oxford, England) · 2020Article
- A Literature Review of Gene Function Prediction by Modeling Gene Ontology.Frontiers in genetics · 2020Review
- Improving protein function prediction using protein sequence and GO-term similarities.Bioinformatics (Oxford, England) · 2019Article
- Evaluating the impact of topological protein features on the negative examples selection.BMC bioinformatics · 2018Article
- deepNF: deep network fusion for protein function prediction.Bioinformatics (Oxford, England) · 2018Article
- BUSCA: an integrative web server to predict subcellular localization of proteins.Nucleic acids research · 2018Article
- NoGOA: predicting noisy GO annotations using evidences and sparse representation.BMC bioinformatics · 2017Article
- Negative example selection for protein function prediction: the NoGO database.PLoS computational biology · 2014Article
- High-resolution functional annotation of human transcriptome: predicting isoform functions by a novel multiple instance-based label propagation method.Nucleic acids research · 2014Article
- Hierarchical ensemble methods for protein function prediction.ISRN bioinformatics · 2014Review
- Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update.F1000Research · 2013Article
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
motivationComputational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction.
resultsWe present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested. AVAILABILITY: Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html
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