Name
Abstract | ||
|---|---|---|
The lack of discipline and consistency in gene naming poses a formidable challenge to re- searchers in locating relevant information sources in the genomics literature. The re- search presented here primarily focuses on how to find the MEDLINE® citations that de- scribe functions of particular genes. We de- veloped new methods and extended current techniques that may help researchers to re- trieve such citations accurately. We further evaluated several machine learning and opti- mization algorithms to identify the sentences describing gene functions in given citations. |
Year | Venue | Keywords |
|---|---|---|
2003 | TREC | decision lists,medline,information retrieval,bayesian networks,machine learning,model averaging,propositional logic,genomics,probabilistic in- ference.,mesh,bayesian network,functional genomics |
Field | DocType | Citations |
Data mining,Information retrieval,Computer science,Functional genomics,Genomics,Optimization algorithm,Gene nomenclature,MEDLINE | Conference | 10 |
PageRank | References | Authors |
1.39 | 9 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehmet Kayaalp | 1 | 254 | 36.49 |
| Alan R. Aronson | 2 | 2551 | 260.67 |
| Susanne M. Humphrey | 3 | 561 | 63.27 |
| Nicholas C. Ide | 4 | 91 | 10.78 |
| Lorraine Tanabe | 5 | 383 | 29.80 |
| Lawrence H. Smith | 6 | 196 | 14.48 |
| Dina Demner Fushman | 7 | 1717 | 147.70 |
| Russell R. Loane | 8 | 30 | 3.36 |
| James G. Mork | 9 | 647 | 65.22 |
| Olivier Bodenreider | 10 | 2715 | 226.05 |