Abstract
Nowadays, it is well-known that there are several genetic alterations that can be employed as genetic markers of prostate cancer. The use of pathways (gene sets) is one of the most promising areas of research in the cancer investigation.
The aim of the present research is to study the influence of the pathways, with the help of models such as recursive partitioning method, to detect the single nucleotide polymorphism of relevance, and consequently the detection of prostate cancer. Data is retrieved from subjects of MCC-Spain database, and are selected as cases and controls, representing a heterogeneous group.
With recursive partitioning method decision trees are built, which allow to prune off the splits that are supposed to be not of interest. Then, with the selected pathways, multivariate adaptive regression spline models are trained, and its performance is assessed in terms of the Area Under Curve (AUC) of the Receiver Operating Characteristics (ROC) curve.
As results, with the help of performance tests, that would be useful for researchers that works with genetic datasets, a dimensional reduction and tuning of the parameters for the models is determined.
In the case of our research, a total of 12 SNPs were found as the most relevant of the above mentioned database for the prostate cancer detection.
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Gutiérrez, D.Á. et al. (2018). A Multiregressive Approach for SNPs Identification in Prostate Cancer. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_39
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