| Title |
Literature mining of protein-residue associations with graph rules learned through distant supervision
|
|---|---|
| Published in |
Journal of Biomedical Semantics, October 2012
|
| DOI | 10.1186/2041-1480-3-s3-s2 |
| Pubmed ID | |
| Authors |
KE Ravikumar, Haibin Liu, Judith D Cohn, Michael E Wall, Karin Verspoor |
| Abstract |
We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 2 | 7% |
| Unknown | 26 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 32% |
| Student > Master | 4 | 14% |
| Researcher | 3 | 11% |
| Student > Doctoral Student | 2 | 7% |
| Professor | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 8 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 16 | 57% |
| Biochemistry, Genetics and Molecular Biology | 2 | 7% |
| Agricultural and Biological Sciences | 2 | 7% |
| Unknown | 8 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 06 January 2013.
All research outputs
#15,253,344
of 22,681,577 outputs
Outputs from Journal of Biomedical Semantics
#238
of 364 outputs
Outputs of similar age
#108,290
of 172,607 outputs
Outputs of similar age from Journal of Biomedical Semantics
#2
of 12 outputs
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So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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