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TEXTQUEST: DOCUMENT CLUSTERING OF MEDLINE ABSTRACTS FOR CONCEPT DISCOVERY IN MOLECULAR BIOLOGY

I. ILIOPOULOS, A. J. ENRIGHT, C. A. OUZOUNIS
2000 Biocomputing 2001  
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

Rajesh Nair, Burkhard Rost
2004 The AI Magazine  
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]

P. Kankar, S. Adak, A. Sarkar, K. Murari, G. Sharma
2002 Proceedings of the 2002 SIAM International Conference on Data Mining  
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