Name
Abstract | ||
|---|---|---|
Background: Massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene symbols is a major bottleneck. Results: We developed a simple thesaurus-based disambiguation algorithm that can operate with very little training data. The thesaurus comprises the information from five human genetic databases and MeSH. The extent of the homonym problem for human gene symbols is shown to be substantial (33% of the genes in our combined thesaurus had one or more ambiguous symbols), not only because one symbol can refer to multiple genes, but also because a gene symbol can have many non-gene meanings. A test set of 52,529 Medline abstracts, containing 690 ambiguous human gene symbols taken from OMIM, was automatically generated. Overall accuracy of the disambiguation algorithm was up to 92.7% on the test set. Conclusion: The ambiguity of human gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings. The proposed disambiguation approach resolves most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions. The algorithm is fast and scalable, enabling gene-symbol disambiguation in massive text mining applications. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1186/1471-2105-6-149 | BMC Bioinformatics |
Keywords | Field | DocType |
bioinformatics,genes,microarrays,text mining,controlled,algorithms,genetics,databases,human genetics | Bottleneck,Text mining,Information retrieval,Computer science,Artificial intelligence,Natural language processing,Bioinformatics,Vocabulary,Gene Symbol | Journal |
Volume | Issue | ISSN |
6 | 1 | 1471-2105 |
Citations | PageRank | References |
41 | 1.03 | 20 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bob J. A. Schijvenaars | 1 | 189 | 10.39 |
| Barend Mons | 2 | 430 | 33.31 |
| Marc Weeber | 3 | 457 | 34.63 |
| Martijn J. Schuemie | 4 | 529 | 34.40 |
| Erik M. Van Mulligen | 5 | 633 | 44.63 |
| Hester M. Wain | 6 | 126 | 19.97 |
| Jan A. Kors | 7 | 635 | 37.25 |