Name
Abstract | ||
|---|---|---|
Objective To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods. Methods We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article. Results We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder-gene link are more extensive and accurate than other general purpose gene-to-disorder association databases. Conclusions We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene-disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1136/amiajnl-2012-001563 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
disorder genes,gene-disease relationships,literature mining,pubmed search tool,bibliome,algorithms,data mining,genes | Data mining,Scientific literature,Disease,Gene,Candidate gene,General purpose,Information retrieval,Bibliome,Medicine | Journal |
Volume | Issue | ISSN |
21 | 3 | 1067-5027 |
Citations | PageRank | References |
3 | 0.40 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Yoon Jung | 1 | 297 | 31.94 |
| Todd F. DeLuca | 2 | 41 | 4.08 |
| Tristan H Nelson | 3 | 3 | 0.40 |
| Dennis P. Wall | 4 | 169 | 22.14 |