ArticleJournal of biomedical semantics2015
Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct.
Article in Journal of biomedical semantics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).Methods in molecular biology (Clifton, N.J.) · 2025Article
- Predicting protein functions by applying predicate logic to biomedical literature.BMC bioinformatics · 2019Article
- Gold-standard ontology-based anatomical annotation in the CRAFT Corpus.Database : the journal of biological databases and curation · 2017Article
- Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition.Journal of biomedical semantics · 2016Article
- Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts.BMC medical informatics and decision making · 2016Article
- Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks.Methods (San Diego, Calif.) · 2016Article
- Predicting the functions of a protein from its ability to associate with other molecules.BMC bioinformatics · 2016Article
- PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.F1000Research · 2015Article
- Special issue on bio-ontologies and phenotypes.Journal of biomedical semantics · 2015Article
- A close look at protein function prediction evaluation protocols.GigaScience · 2015Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine model, GOstruct. We find that even simple literature based features are useful for predicting human protein function (F-max: Molecular Function =0.408, Biological Process =0.461, Cellular Component =0.608). One advantage of using literature features is their ability to offer easy verification of automated predictions. We find through manual inspection of misclassifications that some false positive predictions could be biologically valid predictions based upon support extracted from the literature. Additionally, we present a "medium-throughput" pipeline that was used to annotate a large subset of co-mentions; we suggest that this strategy could help to speed up the rate at which proteins are curated.
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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.