Biclustering is an intrinsically challenging andhighly complex problem, particularly studied in thebiology field, where the goal is to simultaneouslycluster genes and samples of an expression data matrix.In this paper we present a novel approach togene expression biclustering by providing a binaryFactor Graph formulation to such problem. In moredetail, we reformulate biclustering as a sequentialsearch for single biclusters and use an efficient optimizationprocedure based on the Max Sum algorithm.Such approach, drastically alleviates thescaling issues of previous approaches for biclusteringbased on Factor Graphs obtaining significantlymore accurate results on synthetic datasets. A furtheranalysis on two real-world datasets confirmsthe potentials of the proposed methodology whencompared to alternative state of the art methods.

Biclustering gene expressions using factor graphs and the max-sum algorithm

Denitto, Matteo;FARINELLI, Alessandro;BICEGO, Manuele
2015-01-01

Abstract

Biclustering is an intrinsically challenging andhighly complex problem, particularly studied in thebiology field, where the goal is to simultaneouslycluster genes and samples of an expression data matrix.In this paper we present a novel approach togene expression biclustering by providing a binaryFactor Graph formulation to such problem. In moredetail, we reformulate biclustering as a sequentialsearch for single biclusters and use an efficient optimizationprocedure based on the Max Sum algorithm.Such approach, drastically alleviates thescaling issues of previous approaches for biclusteringbased on Factor Graphs obtaining significantlymore accurate results on synthetic datasets. A furtheranalysis on two real-world datasets confirmsthe potentials of the proposed methodology whencompared to alternative state of the art methods.
Biclustering, Factor Graph, Max-Sum
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/930169
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