Discovering Genes-Diseases Associations From Specialized Literature Using the Grid
IEEE Transactions on Information Technology in BiomedicinePublished 6 November 2008
Alberto Faro, Daniela Giordano, Francesco Maiorana, Concetto Spampinato
Citations28
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TL;DR
A novel method for text mining on the Grid, aimed at pointing out hidden relationships for hypothesis generation and suitable for semi-interactive querying, based on unsupervised clustering and the outputs are visualized with contextual information.
Abstract
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.
Keywords
Computer ScienceBiochemistry, Genetics and Molecular Biology
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