Abstract
The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in oncology. The Affymetrix DMET microarray platform allows to determine the ADME gene variants of a patient and to correlate them with drug-dependent adverse events. We present a bioinformatics tool devoted to the discovery of gene variants correlated to a different response of cancer patients to drugs and able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients. The tool is based on the integration of DMET-Miner and OSAnalyzer. DMET-Miner is a data mining tool able to extract Association Rules from DMET datasets and OSAnalyzer is a software tool able to perform an automatic analysis of DMET data enriched with survival events. After presenting DMET-Miner and OSAnalyzer, we discuss a case study to highlight the usefulness of the pipeline constituted by DMET-Miner and OSAnalyzer when analyzing a large cohort of patients.
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Acknowledgments
This work has been partially funded by the following research project funded by the Italian Ministry of Education and Research (MIUR): “BA2Know-Business Analytics to Know” (PON03PE_00001_1).
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Agapito, G., Guzzi, P.H., Cannataro, M. (2017). Efficient Data Mining Analysis of Genomics and Clinical Data for Pharmacogenomics Applications. In: Petrosino, A., Loia, V., Pedrycz, W. (eds) Fuzzy Logic and Soft Computing Applications. WILF 2016. Lecture Notes in Computer Science(), vol 10147. Springer, Cham. https://doi.org/10.1007/978-3-319-52962-2_21
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DOI: https://doi.org/10.1007/978-3-319-52962-2_21
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