How to link ontologies and protein-protein interactions to literature: text-mining approaches and the BioCreative experience

Abstract

There is an increasing interest in developing ontologies and controlled vocabularies to improve the efficiency and consistency of manual literature curation, to enable more formal biocuration workflow results and ultimately to improve analysis of biological data. Two ontologies that have been successfully used for this purpose are the Gene Ontology (GO) for annotating aspects of gene products and the Molecular Interaction ontology (PSI-MI) used by databases that archive protein–protein interactions. The examination of protein interactions has proven to be extremely promising for the understanding of cellular processes. Manual mapping of information from the biomedical literature to bio-ontology terms is one of the most challenging components in the curation pipeline. It requires that expert curators interpret the natural language descriptions contained in articles and infer their semantic equivalents in the ontology (controlled vocabulary). Since manual curation is a time-consuming process, there is strong motivation to implement text-mining techniques to automatically extract annotations from free text. A range of text mining strategies has been devised to assist in the automated extraction of biological data. These strategies either recognize technical terms used recurrently in the literature and propose them as candidates for inclusion in ontologies, or retrieve passages that serve as evidential support for annotating an ontology term, e.g. from the PSI-MI or GO controlled vocabularies. Here, we provide a general overview of current text-mining methods to automatically extract annotations of GO and PSI-MI ontology terms in the context of the BioCreative (Critical Assessment of Information Extraction Systems in Biology) challenge. Special emphasis is given to protein–protein interaction data and PSI-MI terms referring to interaction detection methods.

The BioGRID interaction database: 2013 update

“…In turn, BioGRID supports the text-mining community by providing a gold-standard collection of manually curated interactions for the BioCreative challenge (19–22), a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. We have also established collaborations with WormBase (23) and the development team for the Textpresso text-mining tool (24).…”

From word models to executable models of signaling networks using automated assembly

“…Natural language has many benefits as a means of expressing mechanistic information: In addition to being familiar, it can concisely capture experimental findings about mechanisms that are ambiguous and incomplete. Extensive work has been performed on the use of software to convert text into computable representations of natural language, and such natural language processing (NLP) tools are used extensively to mine the scientific literature (Krallinger et al , ; Fluck & Hofmann‐Apitius, ). To our knowledge however, natural language has not been widely used as a direct input for mechanistic modeling of biological or chemical processes.…”

Mineração de textos biomédicos: uma revisão bibliométrica

“…Assim, diversos trabalhos tem se dedicado a desenvolver técnicas para extrair anotações automáticas do texto (KRALLINGER et al, 2012). Morchen et al (2008), por exemplo, propõem um método automatizado para anotar documentos, utilizando uma abordagem probabilística.…”