PREDICTION OF PROTEIN INTERACTIONS ON HIV-1–HUMAN PPI DATA USING A NOVEL CLOSURE-BASED INTEGRATED APPROACH

Kartick C. Mondal; Nicolas Pasquier; Anirban Mukhopadhyay; Célia da Costa Pereira; Ujjwal Maulik; Andrea G. B. Tettamanzi

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Paper

PREDICTION OF PROTEIN INTERACTIONS ON HIV-1–HUMAN PPI DATA USING A NOVEL CLOSURE-BASED INTEGRATED APPROACH

Topics: Algorithms and Software Tools; Biostatistics and Stochastic Models; Data Mining and Machine Learning; Pattern Recognition, Clustering and Classification

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 0BIOSTEC, 164-173, 2012 , Vilamoura, Algarve, Portugal

Authors: Kartick C. Mondal ¹ ; Nicolas Pasquier ² ; Anirban Mukhopadhyay ³ ; Célia da Costa Pereira ¹ ; Ujjwal Maulik ⁴ and Andrea G. B. Tettamanzi ⁵

Affiliations: ¹ Université de Nice Sophia Antipolis, France ; ² Université de Nice Sophia Antipolis Nice Sophia-Antipolis, France ; ³ University of Kalyani, India ; ⁴ Jadavpur University, India ; ⁵ Università degli Studi di Milano, Italy

Keyword(s): Frequent closed itemsets, Association rules, Bi-clustering, HIV-1-Human Protein-Protein interactions.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Biostatistics and Stochastic Models ; Data Mining and Machine Learning ; Pattern Recognition, Clustering and Classification

Abstract: Discovering Protein-Protein Interactions (PPI) is a new interesting challenge in computational biology. Identifying interactions among proteins was shown to be useful for finding new drugs and preventing several kinds of diseases. The identification of interactions between HIV-1 proteins and Human proteins is a particular PPI problem whose study might lead to the discovery of drugs and important interactions responsible for AIDS. We present the FIST algorithm for extracting hierarchical bi-clusters and minimal covers of association rules in one process. This algorithm is based on the frequent closed itemsets framework to efficiently generate a hierarchy of conceptual clusters and non-redundant sets of association rules with supporting object lists. Experiments conducted on a HIV-1 and Human proteins interaction dataset show that the approach efficiently identifies interactions previously predicted in the literature and can be used to predict new interactions based on previous biologi cal knowledge. (More)

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Paper citation in several formats:

C. Mondal, K.; Pasquier, N.; Mukhopadhyay, A.; da Costa Pereira, C.; Maulik, U. and G. B. Tettamanzi, A. (2012). PREDICTION OF PROTEIN INTERACTIONS ON HIV-1–HUMAN PPI DATA USING A NOVEL CLOSURE-BASED INTEGRATED APPROACH. In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2012) - BIOINFORMATICS; ISBN 978-989-8425-90-4; ISSN 2184-4305, SciTePress, pages 164-173. DOI: 10.5220/0003769001640173

@conference{bioinformatics12,
author={Kartick {C. Mondal}. and Nicolas Pasquier. and Anirban Mukhopadhyay. and Célia {da Costa Pereira}. and Ujjwal Maulik. and Andrea {G. B. Tettamanzi}.},
title={PREDICTION OF PROTEIN INTERACTIONS ON HIV-1–HUMAN PPI DATA USING A NOVEL CLOSURE-BASED INTEGRATED APPROACH},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2012) - BIOINFORMATICS},
year={2012},
pages={164-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003769001640173},
isbn={978-989-8425-90-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2012) - BIOINFORMATICS
TI - PREDICTION OF PROTEIN INTERACTIONS ON HIV-1–HUMAN PPI DATA USING A NOVEL CLOSURE-BASED INTEGRATED APPROACH
SN - 978-989-8425-90-4
IS - 2184-4305
AU - C. Mondal, K.
AU - Pasquier, N.
AU - Mukhopadhyay, A.
AU - da Costa Pereira, C.
AU - Maulik, U.
AU - G. B. Tettamanzi, A.
PY - 2012
SP - 164
EP - 173
DO - 10.5220/0003769001640173
PB - SciTePress