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Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct
2015
Journal of Biomedical Semantics
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
doi:10.1186/s13326-015-0006-4
pmid:26005564
pmcid:PMC4441003
fatcat:pz7pgludtjgm3ndkcl56ir26am