TXTGate: profiling gene groups with text-based information.
We implemented a framework called TXTGate that combines literature indices of selected public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. By means of tailored vocabularies, term- as well as gene-centric views are offered on selected textual fields and MEDLINE abstracts used in LocusLink and the Saccharomyces Genome Database. Subclustering and links to external resources allow for in-depth analysis of the resulting term profiles.
Glenisson
Patrick
P
Departement Elektrotechniek (ESAT), Faculteit Toegepaste Wetenschappen, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee (Leuven), Belgium.
Coessens
Bert
B
Van Vooren
Steven
S
Mathys
Janick
J
Moreau
Yves
Y
De Moor
Bart
B
D000818:Animals; D016000:Cluster Analysis; D030541:Databases, Genetic / Q000706:statistics & numerical data; D004195:Disease Models, Animal; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D015966:Gene Expression Regulation, Fungal / Q000235:genetics; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D005800:Genes, Fungal / Q000235:genetics; D052138:Genes, Neoplasm / Q000235:genetics; D016681:Genome, Fungal; D015894:Genome, Human; D006801:Humans; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data / Q000639:trends*; D016239:MEDLINE / Q000592:standards / Q000706:statistics & numerical data; D051379:Mice; D012440:Saccharomyces / Q000235:genetics; D012468:Salivary Gland Neoplasms / Q000235:genetics; D014825:Vocabulary