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A Deep Embedded Refined Clustering Approach for Breast Cancer Distinction based on DNA Methylation. (arXiv:2102.09563v1 [cs.LG])

Epigenetic alterations have an important role in the development of several
types of cancer. Epigenetic studies generate a large amount of data, which
makes it essential to develop novel models capable of dealing with large-scale
data. In this work, we propose a deep embedded refined clustering method for
breast cancer differentiation based on DNA methylation. In concrete, the deep
learning system presented here uses the levels of CpG island methylation
between 0 and 1. The proposed approach is composed of two main stages. The
first stage consists in the dimensionality reduction of the methylation data
based on an autoencoder. The second stage is a clustering algorithm based on
the soft-assignment of the latent space provided by the autoencoder. The whole
method is optimized through a weighted loss function composed of two terms:
reconstruction and classification terms. To the best of the authors’ knowledge,
no previous studies have focused on the dimensionality reduction algorithms
linked to classification trained end-to-end for DNA methylation analysis. The
proposed method achieves an unsupervised clustering accuracy of 0.9927 and an
error rate (%) of 0.73 on 137 breast tissue samples. After a second test of the
deep-learning-based method using a different methylation database, an accuracy
of 0.9343 and an error rate (%) of 6.57 on 45 breast tissue samples is
obtained. Based on these results, the proposed algorithm outperforms other
state-of-the-art methods evaluated under the same conditions for breast cancer
classification based on DNA methylation data.



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