Discovering genes-diseases associations from specialized literature using the grid.
This paper proposes a novel method for text mining on the Grid, aimed at pointing out hidden relationships for hypothesis generation and suitable for semi-interactive querying. The method is based on unsupervised clustering and the outputs are visualized with contextual information. Grid implementation is crucial for feasibility. We demonstrate it with a mining run for discovering genes-diseases associations from bibliographic sources and annotated databases. The proposed methodology is in view of a Grid architecture specialized in bioinformatics mining tasks. Some performance considerations are provided.
Faro
Alberto
A
Dipartimento di Ingegneria Informatica e Telecomunicazioni, Università di Catania, 95125 Catania, Italy. afaro@diit.unict.it
Giordano
Daniela
D
Maiorana
Francesco
F
Spampinato
Concetto
C
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003550:Cystic Fibrosis / Q000235:genetics; D016206:Databases, Bibliographic; D004194:Disease / Q000235:genetics*; D020022:Genetic Predisposition to Disease