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Learning Association Rules for Pharmacogenomic Studies

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New Frontiers in Mining Complex Patterns (NFMCP 2017)

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Abstract

The better understanding of variants of the genomes may improve the knowledge on the causes of the individuals’ different responses to drugs. The Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform offers the possibility to determine the gene variants of a patient and correlate them with drug-dependent adverse events. The analysis of DMET data is a growing research area. Existing approaches span from the use of simple statistical tests to more complex strategies based, for instance, on learning association rules. To support the analysis, we developed GenotypeAnalytics, a RESTFul-based software service able to automatically extract association rules from DMET datasets. GenotypeAnalytics is based on an optimised algorithm for learning rules that can outperform general purpose platforms.

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References

  1. Meyer, U.A.: Pharmacogenetics and adverse drug reactions. Lancet 356(9242), 1667–1671 (2000)

    Article  Google Scholar 

  2. Li, J., Zhang, L., Zhou, H., Stoneking, M., Tang, K.: Global patterns of genetic diversity and signals of natural selection for human ADME genes. Hum. Mol. Genet. 20(3), 528–540 (2011)

    Article  Google Scholar 

  3. Lombardi, G., Rumiato, E., Bertorelle, R., Saggioro, D., Farina, P., Della Puppa, A., Zustovich, F., Berti, F., Sacchetto, V., Marcato, R., et al.: Clinical and genetic factors associated with severe hematological toxicity in Glioblastoma patients during Radiation Plus Temozolomide treatment: a prospective study. Am. J. Clin. Oncol. 10, 1097 (2013)

    Google Scholar 

  4. Di Martino, M.T., Arbitrio, M., Guzzi, P.H., Leone, E., Baudi, F., Piro, E., Prantera, T., Cucinotto, I., Calimeri, T., Rossi, M., Veltri, P., Cannataro, M., Tagliaferri, P., Tassone, P.: A peroxisome proliferator-activated receptor gamma (PPARG) polymorphism is associated with Zoledronic acid-related Osteonecrosis of the jaw in multiple Myeloma patients: analysis by DMET microarray profiling. Br. J. Haematol. 154, 529–533 (2011)

    Article  Google Scholar 

  5. Guzzi, P.H., Agapito, G., Milano, M., Cannataro, M.: Methodologies and experimental platforms for generating and analysing microarray and mass spectrometry-based omics data to support P4 medicine. Briefings Bioinf. 17(4), 553–561 (2015)

    Article  Google Scholar 

  6. Arbitrio, M., Di Martino, M.T., Barbieri, V., Agapito, G., Guzzi, P.H., Botta, C., Iuliano, E., Scionti, F., Altomare, E., Codispoti, S., et al.: Identification of polymorphic variants associated with erlotinib-related skin toxicity in advanced non-small cell lung cancer patients by DMET microarray analysis. Cancer Chemother. Pharmacol. 77(1), 205–209 (2016)

    Article  Google Scholar 

  7. Di Martino, M.T., Arbitrio, M., Leone, E., Guzzi, P.H., Saveria Rotundo, M., Ciliberto, D., Tomaino, V., Fabiani, F., Talarico, D., Sperlongano, P., Doldo, P., Cannataro, M., Caraglia, M., Tassone, P., Tagliaferri, P.: Single nucleotide polymorphisms of ABCC5 and ABCG1 transporter genes correlate to irinotecan-associated gastrointestinal toxicity in colorectal cancer patients: a DMET microarray profiling study. Cancer Biol. Ther. 12(9), 780–787 (2011)

    Article  Google Scholar 

  8. Guzzi, P.H., Cannataro, M.: \(\mu \)-CS: an extension of the TM4 platform to manage Affymetrix binary data. BMC Bioinform. 11(1), 315 (2010)

    Article  Google Scholar 

  9. Arbitrio, M., Di Martino, M.T., Scionti, F., Agapito, G., Guzzi, P.H., Cannataro, M., Tassone, P., Tagliaferri, P.: DMET™ (Drug Metabolism Enzymes and Transporters): a pharmacogenomic platform for precision medicine. Oncotarget 7(33), 54028 (2016)

    Article  Google Scholar 

  10. Guzzi, P., Agapito, G., Di Martino, M., Arbitrio, M., Tassone, P., Tagliaferri, P., Cannataro, M.: DMET-analyzer: automatic analysis of Affymetrix DMET data. BMC Bioinform. 13(1), 258 (2012)

    Article  Google Scholar 

  11. Rumiato, E., Boldrin, E., Amadori, A., Saggioro, D.: DMET (Drug-Metabolizing Enzymes and Transporters) microarray analysis of colorectal cancer patients with severe 5-fluorouraci-induced toxicity. Cancer Chemother. Pharmacol. 72(2), 483–488 (2013)

    Article  Google Scholar 

  12. Agrawal, R., Imieliński, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases, vol. 22, pp. 207–216. ACM, New York (1993)

    Google Scholar 

  13. Guzzi, P.H., Agapito, G., Cannataro, M.: coreSNP: parallel processing of microarray data. IEEE Trans. Comput. 63(12), 2961–2974 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Di Martino, M.T., Guzzi, P.H., Caracciolo, D., Agnelli, L., Neri, A., Walker, B.A., Morgan, G.J., Cannataro, M., Tassone, P., Tagliaferri, P.: Integrated analysis of microRNAs, transcription factors and target genes expression discloses a specific molecular architecture of hyperdiploid multiple myeloma. Oncotarget 6(22), 19132 (2015)

    Article  Google Scholar 

  15. Di Martino, M.T., Scionti, F., Sestito, S., Nicoletti, A., Arbitrio, M., Guzzi, P.H., Talarico, V., Altomare, F., Sanseviero, M.T., Agapito, G., et al.: Genetic variants associated with gastrointestinal symptoms in fabry disease. Oncotarget 7(52), 85895 (2016)

    Google Scholar 

  16. Zaki, M.J., Hsiao, C.J.: CHARM: an efficient algorithm for closed itemset mining. In: Proceedings of the 2002 SIAM International Conference on Data Mining. SIAM, pp. 457–473 (2002)

    Google Scholar 

  17. Pei, J., Han, J., Mao, R., et al.: CLOSET: an efficient algorithm for mining frequent closed itemsets. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, vol. 4, pp. 21–30 (2000)

    Google Scholar 

  18. Agapito, G., Milano, M., Guzzi, P.H., Cannataro, M.: Extracting cross-ontology weighted association rules from gene ontology annotations. IEEE/ACM Trans. Comput. Biol. Bioinform. 13(2), 197–208 (2016)

    Article  Google Scholar 

  19. Agapito, G., Cannataro, M., Guzzi, P.H., Milano, M.: Using go-war for mining cross-ontology weighted association rules. Comput. Methods Programs Biomed. 120(2), 113–122 (2015)

    Article  Google Scholar 

  20. Agapito, G., Cannataro, M., Guzzi, P.H., Marozzo, F., Talia, D., Trunfio, P.: Cloud4SNP: distributed analysis of SNP microarray data on the cloud. In: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, p. 468. ACM (2013)

    Google Scholar 

  21. Agapito, G., Guzzi, P.H., Cannataro, M.: DMET-Miner: Efficient discovery of association rules from pharmacogenomic data. Journal of biomedical informatics 56, 273–283 (2015)

    Article  Google Scholar 

  22. Agapito, G., Botta, C., Guzzi, P.H., Arbitrio, M., Di Martino, M.T., Tassone, P., Tagliaferri, P., Cannataro, M.: OSAnalyzer: a bioinformatics tool for the analysis of gene polymorphisms enriched with clinical outcomes. Microarrays 5(4), 24 (2016)

    Article  Google Scholar 

  23. Sissung, T., English, B., Venzon, D., Figg, W., Deeken, J.: Clinical pharmacology and pharmacogenetics in a genomics era: the DMET platform. Pharmacogenomics 11, 89–103 (2010)

    Article  Google Scholar 

  24. Marozzo, F., Talia, D., Trunfio, P.: A cloud framework for big data analytics workflows on Azure. In: Cloud Computing and Big Data. Advances in Parallel Computing, vol. 23, pp. 182–191. IOS Press (2013). https://doi.org/10.3233/978-1-61499-322-3-182

  25. Marozzo, F., Talia, D., Trunfio, P.: Using clouds for scalable knowledge discovery applications. In: Caragiannis, I., Alexander, M., Badia, R.M., Cannataro, M., Costan, A., Danelutto, M., Desprez, F., Krammer, B., Sahuquillo, J., Scott, S.L., Weidendorfer, J. (eds.) Euro-Par 2012. LNCS, vol. 7640, pp. 220–227. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36949-0_25

    Chapter  Google Scholar 

  26. Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: SPMF: a java open-source pattern mining library. J. Mach. Learn. Res. 15(1), 3389–3393 (2014)

    MATH  Google Scholar 

  27. Borgelt, C.: Frequent item set mining. Wiley Interdisc. Rev. Data Mining Knowl. Discovery 2(6), 437–456 (2012)

    Article  Google Scholar 

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Acknowledgements

This work has been partially funded by the following research projects:

– “BA2Know-Business Analytics to Know” (PON03PE_00001_1), funded by the Italian Ministry of Education and Research (MIUR)

– INdAM - GNCS Project 2017: “Efficient Algorithms and Techniques for the Organization, Management and Analysis of Biological Big Data”.

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Authors and Affiliations

  1. Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, Catanzaro, Italy

    Giuseppe Agapito, Pietro H. Guzzi & Mario Cannataro

  2. Data Analytics Research Centre, University Magna Græcia of Catanzaro, Catanzaro, Italy

    Giuseppe Agapito, Pietro H. Guzzi & Mario Cannataro

Authors
  1. Giuseppe Agapito
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  2. Pietro H. Guzzi
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  3. Mario Cannataro
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Corresponding author

Correspondence to Mario Cannataro .

Editor information

Editors and Affiliations

  1. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. University of Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  3. CNR, Rende, Italy

    Giuseppe Manco

  4. CNR, Rende, Italy

    Elio Masciari

  5. University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Ras

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Agapito, G., Guzzi, P.H., Cannataro, M. (2018). Learning Association Rules for Pharmacogenomic Studies. In: Appice, A., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2017. Lecture Notes in Computer Science(), vol 10785. Springer, Cham. https://doi.org/10.1007/978-3-319-78680-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-78680-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78679-7

  • Online ISBN: 978-3-319-78680-3

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