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

2012

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 biological knowledge.

References

  1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A. I. (1996). Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, pages 307-328. AAAI/MIT Press.
  2. Arkin, M. R. and Wells, J. A. (2004). Small-molecule inhibitors of protein-protein interactions: Progressing towards the dream. Nat. Rev. Drug Discov., 3:301- 317.
  3. Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., and Lakhal, L. (2000). Mining minimal non-redundant association rules using frequent closed itemsets. In Proc. CL, pages 972-986.
  4. Bell, L., Chowdhary, R., Liu, J. S., Niu, X., and Zhang, J. (2011). Integrated bio-entity network: A system for biological knowledge discovery. PLoS ONE, 6.
  5. Ben-Hur, A. and Noble, W. S. (2005). Kernel methods for predicting protein-protein interactions. Bioinformatics, 21(1):38-46.
  6. Ceglar, A. and Roddick, J. (2006). Association mining. ACM Comput. Surv., 2(38).
  7. Chirmule, N., Oyaizu, N., Saxinger, C., and Pahwa, S. (1994). Nef protein of hiv-1 has b-cell stimulatory activity. AIDS, 6(8):733-4.
  8. Fu, W., Sanders-Beer, B. E., Katz, K. S., Maglott, D. R., Pruitt, K. D., and Ptak, R. G. (2009). Human immunodeficiency virus type 1, human protein interaction database at ncbi. Nucl. Acids Res., 37:417-422.
  9. Gahery, H., Figueiredo, S., Texier, C., and et al. (2007). Hla-dr-restricted peptides identified in the nef protein can induce hiv type 1-specific il-2/ifn-gammasecreting cd4+ and cd4+ /cd8+ t cells in humans after lipopeptide vaccination. AIDS Res. and Hum. Retroviruses, 3(23):427-37.
  10. Ganter, B. and Wille, R. (1999). Formal Concept Analysis: Mathematical Foundations. Springer.
  11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Expl., 1(11).
  12. Hamrouni, T., Yahia, S. B., and Nguifo, E. M. (2006). Succinct system of minimal generators: A thorough study, limitations and new definitions. In Proc. CLA, pages 80-95.
  13. Jansen, R., H. Yu, D. G., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M., Greenblatt, J. F., and Gerstein, M. (2003). A bayesian networks approach for predicting protein-protein interactions from genomic data. Science, 302:449-453.
  14. Konig, R., Zhou, Y., Elleder, D., and et al. (2008). Global analysis of host-pathogen interactions that regulate early-stage hiv-1 replication. Cell, 135:49-60.
  15. Lin, N., Wu, B., Jansen, R., Gerstein, M., and Zhao, H. (2004). Information assessment on predicting proteinprotein interactions. BMC Bioinformatics, 5(154).
  16. Madeira, S. C. and Oliveira, A. L. (2004). Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Trans. Comput. Biol. Bioinform., 1:24-45.
  17. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., and Eils, R. (2010). Mining association rules from hivhuman protein interaction. In Proc. ICSMB, pages 344-348.
  18. Pasquier, N., Bastide, Y., Taouil, R., and Lakhal, L. (1999). Efficient mining of association rules using closed itemset lattices. Inform. Systems, 1(24):25-46.
  19. Pasquier, N., Taouil, R., Bastide, Y., Stumme, G., and Lakhal, L. (2005). Generating a condensed representation for association rules. J. Intell. Inf. Syst., 1(24):29-60.
  20. Ptak, R. G., Fu, W., Sanders-Beer, B. E., Dickerson, J. E., Pinney, J. W., Robertson, D. L., Rozanov, M. N., Katz, K. S., Maglott, D. R., Pruitt, K. D., and Dieffenbach, C. W. (2008). Cataloguing the hiv-1 human protein interaction network. AIDS Res. and Hum. Retrov., 4(12):1497-1502.
  21. Qi, Y., Klein-Seetharaman, J., and Bar-Joseph, Z. (2007). A mixture of feature experts approach for proteinprotein interaction prediction. BMC Bioinform., 8.
  22. Scala, G. (1994). The expression of the interleukin 6 gene is induced by the human immunodeficiency virus 1 tat protein. J. Exp. Medicine, 3(179):961-971.
  23. Tastan, O., Qi, Y., Carbonell, J., and Klein-Seetharaman, J. (2009). Prediction of interactions between hiv-1 and human proteins by information integration. In Proc. PSB, pages 516-527.
  24. Westendorp, M. O., Li-Weber, M., Frank, R. W., and Krammer, P. H. (1994). Human immunodeficiency virus type 1 tat upregulates interleukin-2 secretion in activated t cells. J. of Virology, 7(68):4177-4185.
  25. Yamanishi, Y., Vert, J. P., and Kanehisa, M. (2004). Protein network inference from multiple genomic data: A supervised approach. Bioinformatics, 20:363-370.
  26. Yang, Y., Webb, G., and Wu, X. (2010). Discretization methods. In Data Mining and Knowledge Discovery Handbook, Part 1, pages 101-116.
  27. Zaki, M. J. (2000). Generating non-redundant association rules. In Proc. KDD, pages 34-43.
  28. Zaki, M. J. (2004). Mining non-redundant association rules. Data Min. Knowl. Disc., 9(23):223-248.
  29. Zhang, L., Wong, S., King, O., and Roth, F. (2004). Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinform., 5(38).
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Paper Citation


in Harvard Style

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 - Volume 1: BIOINFORMATICS, (BIOSTEC 2012) ISBN 978-989-8425-90-4, pages 164-173. DOI: 10.5220/0003769001640173


in Bibtex Style

@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 - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)},
year={2012},
pages={164-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003769001640173},
isbn={978-989-8425-90-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)
TI - PREDICTION OF PROTEIN INTERACTIONS ON HIV-1–HUMAN PPI DATA USING A NOVEL CLOSURE-BASED INTEGRATED APPROACH
SN - 978-989-8425-90-4
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