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

Overview of attention for article published in BMC Bioinformatics, June 2015
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  • Average Attention Score compared to outputs of the same age and source

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6 X users

Citations

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23 Dimensions

Readers on

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104 Mendeley
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4 CiteULike
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Title
Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/s12859-015-0609-x
Pubmed ID
Authors

Komandur Elayavilli Ravikumar, Kavishwar B. Wagholikar, Dingcheng Li, Jean-Pierre Kocher, Hongfang Liu

Abstract

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Spain 1 <1%
Japan 1 <1%
Unknown 100 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 25%
Student > Ph. D. Student 20 19%
Student > Master 7 7%
Student > Bachelor 7 7%
Student > Doctoral Student 5 5%
Other 20 19%
Unknown 19 18%
Readers by discipline Count As %
Computer Science 35 34%
Agricultural and Biological Sciences 14 13%
Biochemistry, Genetics and Molecular Biology 9 9%
Medicine and Dentistry 5 5%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Other 12 12%
Unknown 25 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 April 2016.
All research outputs
#7,710,091
of 23,607,611 outputs
Outputs from BMC Bioinformatics
#3,049
of 7,409 outputs
Outputs of similar age
#91,101
of 267,602 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 118 outputs
Altmetric has tracked 23,607,611 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,409 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,602 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.