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Issue No. 02 - March-April (2013 vol. 10)
ISSN: 1545-5963
pp: 423-435
Ujjwal Maulik , Dept. of Comput. Sci. & Eng., Jadavpur Univ., Kolkata, India
Anirban Mukhopadhyay , Dept. of Comput. Sci. & Eng., Univ. of Kalyani, Kalyani, India
Malay Bhattacharyya , Dept. of Comput. Sci. & Eng., Univ. of Kalyani, Kalyani, India
Lars Kaderali , Inst. for Med. Inf. & Biometry, Dresden Univ. of Technol., Dresden, Germany
Benedikt Brors , Dept. of Theor. Bioinf., Univ. of Heidelberg, Heidelberg, Germany
Sanghamitra Bandyopadhyay , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
Roland Eils , Dept. of Theor. Bioinf., Univ. of Heidelberg, Heidelberg, Germany
In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm-based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs.
Proteins, Humans, Bipartite graph, Biological cells, Optimization, Linear programming, Bioinformatics

U. Maulik et al., "Mining Quasi-Bicliques from HIV-1-Human Protein Interaction Network: A Multiobjective Biclustering Approach," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 423-435, 2013.
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