Experimental comparison of biclustering algorithms for PPI networks

Authors:
Vera Tomaino;Pietro H. Guzzi;Mario Cannataro;Pierangelo Veltri
Affiliations:
University Magna Graecia;University Magna Graecia;University Magna Graecia;University Magna Graecia
Venue:
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Year:
2010

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Abstract

Proteins play their role by interacting with each other. The set of interactions in a single organism is called protein interaction network. Protein Interaction Networks (also shortly PINs) can be represented as graphs and consequently as adjacency matrices. From biological point of view functional meanings may be related to subsets of a PIN. Given a PIN, it is relevant studying and identifying its biological meaningful subsets (submatrices of the adiacency one), also called functional modules. Given a PIN, clustering and biclustering algorithms may be used to define PIN subset and thus (possible) functional modules. Such algorithms are also used to measure similarities between extracted PIN subsets (i.e. submatrices). In this work we study four existing biclustering algorithms, and analyze their ability in identifying biological meaningful PIN subsets. Tests have been performed on two available PINs dataset.