TY - JOUR
T1 - Machine Learning in Epigenomics
T2 - Insights into Cancer Biology and Medicine
AU - Arslan, Emre
AU - Schulz, Jonathan
AU - Rai, Kunal
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12
Y1 - 2021/12
N2 - The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.
AB - The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.
KW - Cancer
KW - Chromatin
KW - Deep learning
KW - Epigenomics
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85110200802&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85110200802&partnerID=8YFLogxK
U2 - 10.1016/j.bbcan.2021.188588
DO - 10.1016/j.bbcan.2021.188588
M3 - Review article
C2 - 34245839
AN - SCOPUS:85110200802
SN - 0304-419X
VL - 1876
JO - Biochimica et Biophysica Acta - Reviews on Cancer
JF - Biochimica et Biophysica Acta - Reviews on Cancer
IS - 2
M1 - 188588
ER -