Chapter

Digital Human Modeling

Volume 4561 of the series Lecture Notes in Computer Science pp 606-615

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

  • Filippo GeraciAffiliated withCNR, Istituto di Informatica e Telematica, via Moruzzi 1, 56124, Pisa, (Italy)Dipartimento di Ingegneria dell’Informazione, Università di Siena, Via Roma 56 - 53100 Siena, (Italy)
  • , Mauro LeonciniAffiliated withCNR, Istituto di Informatica e Telematica, via Moruzzi 1, 56124, Pisa, (Italy)Dipartimento di Ingegneria dell’Informazione, Università di Modena e Reggio Emilia, Via Vignolese 905 - 41100 Modena (Italy)
  • , Manuela MontangeroAffiliated withCNR, Istituto di Informatica e Telematica, via Moruzzi 1, 56124, Pisa, (Italy)Dipartimento di Ingegneria dell’Informazione, Università di Modena e Reggio Emilia, Via Vignolese 905 - 41100 Modena (Italy)
  • , Marco PellegriniAffiliated withCNR, Istituto di Informatica e Telematica, via Moruzzi 1, 56124, Pisa, (Italy)
  • , M. Elena RendaAffiliated withCNR, Istituto di Informatica e Telematica, via Moruzzi 1, 56124, Pisa, (Italy)

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Abstract

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 k-center 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.