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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Multiregressive Approach for SNPs Identification in Prostate Cancer

Authors : David Álvarez Gutiérrez, Fernando Sánchez Lasheras, Sergio Luis Suárez Gómez, Jesús Daniel Santos, Adonina Tardón, Guillermo González Tardón, Carmen González Donquiles, Vicente Martín Sánchez

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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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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previous chapter A Methodology for the Detection of Relevant Single Nucleotide Polymorphism in Prostate Cancer by Means of Multivariate Adaptive Regression Splines and Backpropagation Artificial Neural Networks
next chapter Comparison of the Periimplant Bone Stress Distribution on Three Fixed Partial Supported Prosthesis Designs Under Different Loading. A 3D Finite Element Analysis
Literature
1.
Schnoeller, T.J., Jentzmik, F., Schrader, A.J., Steinestel, J.: Influence of serum cholesterol level and statin treatment on prostate cancer aggressiveness. Oncotarget 8, 47110–47120 (2017)
2.
Tsodikov, A., Gulati, R., de Carvalho, T.M., Heijnsdijk, E.A.M., Hunter-Merrill, R.A., Mariotto, A.B., de Koning, H.J., Etzioni, R.: Is prostate cancer different in black men? Answers from 3 natural history models. Cancer 123, 2312–2319 (2017)CrossRef
3.
Ordóñez-Mena, J.M., Schöttker, B., Mons, U., Jenab, M., Freisling, H., Bueno-de-Mesquita, B., O’Doherty, M.G., Scott, A., Kee, F., Stricker, B.H., et al.: Quantification of the smoking-associated cancer risk with rate advancement periods: meta-analysis of individual participant data from cohorts of the CHANCES consortium. BMC Med. 14(1), 62 (2016)CrossRef
4.
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984). (Case Descr. Featur. Subset Correct Missed FA Misclass 1, 1–3 (1993))
5.
Fallis, A.: An introduction to recursive partitioning using the rpart routines. J. Chem. Inf. Model. 53(9), 1689–1699 (2013)
6.
LeBlanc, M., Crowley, J.: Relative risk trees for censored survival data. Biometrics 48, 411–425 (1992)CrossRef
7.
8.
Osborn, J., Juez, F.J.D.C., Guzman, D., Butterley, T., Myers, R., Guesalaga, A., Laine, J.: Using artificial neural networks for open-loop tomography. Opt. Express 20(3), 2420–2434 (2012)CrossRef
9.
Vilán, J.A.V., Fernández, J.R.A., Nieto, P.J.G., Lasheras, F.S., de Cos Juez, F.J., Muñiz, C.D.: Support vector machines and multilayer perceptron networks used to evaluate the cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain). Water Resour. Manag. 27(9), 3457–3476 (2013)CrossRef
10.
Osborn, J., Guzman, D., Juez, F.J.D.C., Basden, A.G., Morris, T.J., Gendron, E., Butterley, T., Myers, R.M., Guesalaga, A., Lasheras, F.S., Victoria, M.G., Rodríguez, M.L.S., Gratadour, D., Rousset, G.: Open-loop tomography with artificial neural networks on CANARY: On-sky results. Mon. Not. R. Astron. Soc. 441(3), 2508–2514 (2014)CrossRef
11.
Sánchez, A.S., Fernández, P.R., Lasheras, F.S., de Cos Juez, F.J., Nieto, P.J.G.: Prediction of work-related accidents according to working conditions using support vector machines. Appl. Math. Comput. 218(7), 3539–3552 (2011)MathSciNet
12.
Menéndez, L.Á., de Cos Juez, F.J., Lasheras, F.S., Riesgo, J.A.Á.: Artificial neural networks applied to cancer detection in a breast screening programme. Math. Comput. Model. 52(7), 983–991 (2010)MathSciNetCrossRefMATH
13.
Sánchez-Lasheras, F., de Andrés, J., Lorca, P., de Cos Juez, F.J.: A hybrid device for the solution of sampling bias problems in the forecasting of firms’ bankruptcy. Expert Syst. Appl. 39(8), 7512–7523 (2012)CrossRef
14.
González-Gutiérrez, C., Santos, J.D., Martínez-Zarzuela, M., Basden, A.G., Osborn, J., Díaz-Pernas, F.J., De Cos Juez, F.J.: Comparative study of neural network frameworks for the next generation of adaptive optics systems. Sensors 17(6), 1263 (2017)CrossRef
15.
Castaño-Vinyals, G., Aragonés, N., Pérez-Gómez, B., Martín, V., Llorca, J., Moreno, V., Altzibar, J.M., Ardanaz, E., De Sanjosé, S., Jiménez-Moleón, J.J., et al.: Population-based multicase-control study in common tumors in Spain (MCC-Spain): rationale and study design. Gac. Sanit. 29(4), 308–315 (2015)CrossRef
Metadata
Title
A Multiregressive Approach for SNPs Identification in Prostate Cancer
Authors
David Álvarez Gutiérrez
Fernando Sánchez Lasheras
Sergio Luis Suárez Gómez
Jesús Daniel Santos
Adonina Tardón
Guillermo González Tardón
Carmen González Donquiles
Vicente Martín Sánchez
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_39

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