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FPF-SB: a Scalable Algorithm for Microarray Gene Expression Data Clustering

Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the scalability of the data processing software for clustering gene expression data into groups with homogeneous expression profile. In this paper we propose FPF-SB, a novel clustering algorithm based on a combination of the Furthest-Point-First (FPF) heuristic for solving the kcenter problem and a stability-based method for determining the number of clusters k. Our algorithm improves the state of the art: it is scalable to large datasets without sacrificing output quality.

International Conference on Human-Computer Interaction (HCI), Beijing, P.R. China, 2007

Autori: Filippo Geraci, Mauro Leoncini, Manuela Montangero, Marco Pellegrini, M. Elena Renda
Autori IIT:

Tipo: Articolo in Atti di convegno internazionale con referee
Area di disciplina: Information Technology and Communication Systems

Attività: Biologia computazionale