An Approach towards Semi-automated Biomedical Literature Curation and Enrichment for a Major Biological Database

 

Fabio Rinaldi¹, Oscar Lithgow-Serrano², Alejandra López-Fuentes², Socorro Gama-Castro², Yalbi I. Balderas-Martínez², Hilda Solano-Lira², and Julio Collado-Vides²

 

¹ Institute of Computational Linguistics, University of Zurich, Switzerland (e-mail: fabio.rinaldi@uzh.ch).

² Computational Genomics Program, Center for Genomic Sciences, Universidad Nacional Autónoma de México Cuernavaca, Morelos, México.

 

Manuscript received on June 18, 2015
Accepted for publication on August 12, 2015
Published on October 15, 2015

 

Abstract

As part of a large-scale biocuration project, we are developing innovative techniques to process the biomedical literature and extract information relevant to specific biological investigations. Biological experts routinely extract core information from the scientific literature using a manual process known as scientific curation. The aim of our activity is to improve the efficiency of this process by leveraging upon natural language processing technologies in a text mining system. There are two lines of investigation that we pursue: (1) finding information relevant for curation and present it in an adaptive interface, and (2) use sentence-similarity techniques to create interlinks across articles in order to allow a process of knowledge discovery.

Key words: Text mining, natural language processing, biocuration.

 

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ACKNOWLEDGMENT

The development of OntoGene/ODIN has been supported by the Swiss National Science Foundation (grant 105315-130558/1, PI: Fabio Rinaldi) and by the Data Science Group at Hoffmann-La Roche, Basel, Switzerland. The development of RegulonDB is supported by NIH grant 1R01GM110597 to Julio Collado Vides.

 

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