Nonlinear Techniques and Ridge Regression as a Combined Approach: Carcinoma Identification Case Study
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Title
Nonlinear Techniques and Ridge Regression as a Combined Approach: Carcinoma Identification Case StudyDate
2023Publisher
MDPIBibliographic citation
Alfonso Perez, G.; Castillo, R. Nonlinear Techniques and Ridge Regression as a Combined Approach: Carcinoma Identification Case Study. Mathematics 2023, 11, 1795. https:// doi.org/10.3390/math11081795Type
info:eu-repo/semantics/articlePublisher version
https://www.mdpi.com/2227-7390/11/8/1795Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
As more genetic information becomes available, such as DNA methylation levels, it becomes increasingly important to have techniques to analyze such data in the context of cancers such as anal and cervical carcinomas. ... [+]
As more genetic information becomes available, such as DNA methylation levels, it becomes increasingly important to have techniques to analyze such data in the context of cancers such as anal and cervical carcinomas. In this paper, we present an algorithm that differentiates between healthy control patients and individuals with anal and cervical carcinoma, using as an input DNA methylation data. The algorithm used a combination of ridge regression and neural networks for the classification task, achieving high accuracy, sensitivity and specificity. The relationship between methylation levels and carcinoma could in principle be rather complex, particularly given that a large number of CpGs could be involved. Therefore, nonlinear techniques (machine learning) were used. Machine learning techniques (nonlinear) can be used to model linear processes, but the opposite (linear techniques simulating nonlinear models) would not likely generate accurate forecasts. The feature selection process is carried out using a combination of prefiltering, ridge regression and nonlinear modeling (artificial neural networks). The model selected 13 CpGs from a total of 450,000 CpGs available per patient with 171 patients in total. The model was also tested for robustness and compared to other more complex models that generated less precise classifications. The model obtained (testing dataset) an accuracy, sensitivity and specificity of 97.69%, 95.02% and 98.26%, respectively. The reduction of the dimensionality of the data, from 450,000 to 13 CpGs per patient, likely also reduced the likelihood of overfitting, which is a very substantial risk in this type of modelling. All 13 CpGs individually generated classification forecasts less accurate than the proposed model. [-]
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Mathematics, 2023, vol. 11, no 8, p. 1795.Funder Name
Ministerio de Ciencia y Tecnología | Universitat Jaume I
Project code
PID2021-1233320B-C21 | UJI-B2022-12
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info:eu-repo/semantics/openAccess
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- INAM_Articles [519]
Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).