loading
Documents

Research.Publish.Connect.

Paper

Authors: Alexander J. Titus 1 ; Carly A. Bobak 2 and Brock C. Christensen 3

Affiliations: 1 Dartmouth School of Graduate and Advanced Studies and Dartmouth Geisel School of Medicine, United States ; 2 Dartmouth School of Graduate and Advanced Studies, United States ; 3 Dartmouth Geisel School of Medicine, United States

ISBN: 978-989-758-280-6

ISSN: 2184-4305

Keyword(s): Deep Learning, DNA Methylation, Breast Cancer, Epigenetics, Variational Autoencoders, TCGA.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Genomics and Proteomics ; Pattern Recognition, Clustering and Classification

Abstract: In the era of precision medicine and cancer genomics, data are being generated so quickly that it is difficult to fully appreciate the extent of what is discoverable. DNA methylation, a chemical modification to DNA, has been shown to be a significant factor in many cancers and is a candidate data source with ample features for model traing. However, the black-box nature of non-linear models, such as those in deep learning, and a lack of accurately labeled ground truth data have limited the same rapid adoption in this space that other methods have experienced. In this article, we discuss the applications of unsupervised learning through the use of variational autoencoders using DNA methylation data and motivate further work with initial results using breast cancer data provided by The Cancer Genome Atlas. We show that a logistic regression classifier trained on the learned latent methylome accurately classifies disease subtype.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.168.112.145

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Titus, A.; Bobak, C. and Christensen, B. (2018). A New Dimension of Breast Cancer Epigenetics - Applications of Variational Autoencoders with DNA Methylation.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3 BIOINFORMATICS: BIOINFORMATICS, ISBN 978-989-758-280-6, ISSN 2184-4305, pages 140-145. DOI: 10.5220/0006636401400145

@conference{bioinformatics18,
author={Alexander J. Titus. and Carly A. Bobak. and Brock C. Christensen.},
title={A New Dimension of Breast Cancer Epigenetics - Applications of Variational Autoencoders with DNA Methylation},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3 BIOINFORMATICS: BIOINFORMATICS,},
year={2018},
pages={140-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006636401400145},
isbn={978-989-758-280-6},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3 BIOINFORMATICS: BIOINFORMATICS,
TI - A New Dimension of Breast Cancer Epigenetics - Applications of Variational Autoencoders with DNA Methylation
SN - 978-989-758-280-6
AU - Titus, A.
AU - Bobak, C.
AU - Christensen, B.
PY - 2018
SP - 140
EP - 145
DO - 10.5220/0006636401400145

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.