Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature

• Published in: BMC bioinformatics Vol. 16; no. 1; p. 185
• Main Authors: Ravikumar, Komandur Elayavilli, Wagholikar, Kavishwar B., Li, Dingcheng, Kocher, Jean-Pierre, Liu, Hongfang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.06.2015; BioMed Central
• Subjects: Algorithms; Computational Biology - methods; Data Mining - methods; Databases, Factual; Disease - genetics; Genetic Variation - genetics; High-Throughput Nucleotide Sequencing; Humans; Medical Subject Headings; Mutation - genetics; Natural Language Processing
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-015-0609-x

Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating discourse level analysis significantly improved the performance of extracting the protein-mutation-disease association. Future work includes the extension of MutD for full text articles.