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2015 ; 16
(ä): 185
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Text mining facilitates database curation - extraction of mutation-disease
associations from Bio-medical literature
#MMPMID26047637
Ravikumar KE
; Wagholikar KB
; Li D
; Kocher JP
; Liu H
BMC Bioinformatics
2015[Jun]; 16
(ä): 185
PMID26047637
show ga
BACKGROUND: 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. RESULTS: 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%. CONCLUSIONS:
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.