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TEXTQUEST: DOCUMENT CLUSTERING OF MEDLINE ABSTRACTS FOR CONCEPT DISCOVERY IN MOLECULAR BIOLOGY
2000
Biocomputing 2001
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We present an algorithm for large-scale document clustering of biological text, obtained from Medline abstracts. ...
Despite the statistical nature of the approach, with minimal semantic analysis, the terms provide a shallow description of the document corpus and support concept discovery. ...
Thus, the method is a combination of document clustering and concept discovery. Evidently, other machine learning methods are possible at this step. 9. ...
doi:10.1142/9789814447362_0038
fatcat:kyp762w4nzctrmapjhrl6hozsq
Annotating Protein Function through Lexical Analysis
2004
The AI Magazine
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TEXTQUEST: Document Clustering of MEDLINE Greece.
Abstracts for Concept Discovery in Molecular Biolo- Ouzounis, C.; Casari, G.; Sander, C.; Tamames, J.; and
gy. ...
MEDLINE abstracts. ...
doi:10.1609/aimag.v25i1.1746
dblp:journals/aim/NairR04
fatcat:eoinvyqphjg4zmk2jvyoiukpfa
MedMeSH Summarizer: Text Mining for Gene Clusters
[chapter]
2002
Proceedings of the 2002 SIAM International Conference on Data Mining
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TextQuest [19] was one of the first of such solutions, but it is geared to summarize documents retrieved in response to a keyword(s) based search on PubMed. ...
Pattern Discovery: MeSH Summary of a Gene Cluster Gene Cluster: Let G = {g 1 , g 2 , . . . , g N } be the given cluster containing N genes, where g j will be used to denote the j th gene in the cluster ...
doi:10.1137/1.9781611972726.32
dblp:conf/sdm/KankarASMS02
fatcat:b64tq6hjmvarnfr7xfhk23voba