Abstract
The available curated data lag behind current biological knowledge contained in the literature. Text mining can assist biologists and curators to locate and access this knowledge, for instance by characterizing the functional profile of publications. Gene Ontology (GO) category assignment in free text already supports various applications, such as powering ontology-based search engines, finding curation-relevant articles (triage) or helping the curator to identify and encode functions. Popular text mining tools for GO classification are based on so called thesaurus-based—or dictionary-based—approaches, which exploit similarities between the input text and GO terms themselves. But their effectiveness remains limited owing to the complex nature of GO terms, which rarely occur in text. In contrast, machine learning approaches exploit similarities between the input text and already curated instances contained in a knowledge base to infer a functional profile. GO Annotations (GOA) and MEDLINE make possible to exploit a growing amount of curated abstracts (97 000 in November 2012) for populating this knowledge base. Our study compares a state-of-the-art thesaurus-based system with a machine learning system (based on a k-Nearest Neighbours algorithm) for the task of proposing a functional profile for unseen MEDLINE abstracts, and shows how resources and performances have evolved. Systems are evaluated on their ability to propose for a given abstract the GO terms (2.8 on average) used for curation in GOA. We show that since 2006, although a massive effort was put into adding synonyms in GO (+300%), our thesaurus-based system effectiveness is rather constant, reaching from 0.28 to 0.31 for Recall at 20 (R20). In contrast, thanks to its knowledge base growth, our machine learning system has steadily improved, reaching from 0.38 in 2006 to 0.56 for R20 in 2012. Integrated in semi-automatic workflows or in fully automatic pipelines, such systems are more and more efficient to provide assistance to biologists.Database URL: http://eagl.unige.ch/GOCat/
Highlights
The available curated data lag behind current biological knowledge contained in the literature (1, 2)
We begin by briefly describing the resources used for the experiments: the Gene Ontology (GO), the GO Annotations (GOA) database that provided both the knowledge base needed for the machine learning and the benchmarks needed for the evaluation, and the BioCreative I test set that was a supplementary benchmark for our evaluations
We first present the evaluation of the current TB classifier (EAGL) and machine learning (ML) classifier (GOCat), along with GoPubMed, for characterizing the functional profile of 50 abstracts published in 2012
Summary
The available curated data lag behind current biological knowledge contained in the literature (1, 2). A large amount of information is generated by research teams and is usually expressed in natural language published in scientific journals; this knowledge needs to be located, integrated and accessed by biologists and curators. In this perspective, text mining solutions could help biologists in keeping up with the literature (3–6). Characterizing the functional profile of a publication, whether it is for triage, for powering ontology-based search engines or integrated in a curation workflow, is one of these promising solutions.
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