Use and Comparison of Machine Learning Techniques to Discern the Protein Patterns of Autoantibodies Present in Women with and without Breast Pathology.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
José-Luis Llaguno-Roque, Rocio-Erandi Barrientos-Martínez, Héctor-Gabriel Acosta-Mesa, Antonia Barranca-Enríquez, Efrén Mezura-Montes, Tania Romo-González
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引用次数: 0

Abstract

Breast cancer (BC) has become a global health problem, ranking first in incidence and fifth in mortality in women around the world. Although there are some diagnostic methods for the disease, these are not sufficiently effective and are invasive. In this work, we discriminated between patients without breast pathology (BP), with benign BP, and with BC based on the band patterns obtained from Western blot strip images of the autoantibody response to antigens of the T47D tumor line using and comparing supervised machine learning techniques to have a sensitive and accurate method. When comparing the aforementioned machine learning techniques, it was found that by obtaining a convolutional neural network architecture from a neuroevolution algorithm, it is possible to automatically discriminate with a classification accuracy of 90.67% between patients with cancer and with/without BP. In the case of discrimination between patients with cancer and without BP, a classification accuracy of 96.67% was obtained with the K-NN algorithm and 95.13% with the convolutional neural network obtained using a neuroevolution algorithm, although these results are not statistically significant. It is concluded that the convolutional neural network obtained by neuroevolution is the method with the best performance with respect to those evaluated in this work.

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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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