Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.
Biochim Biophys Acta Rev Cancer
; 1876(2): 188588, 2021 12.
Article
en En
| MEDLINE
| ID: mdl-34245839
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Biología
/
Epigenómica
/
Aprendizaje Automático
/
Medicina
Límite:
Humans
Idioma:
En
Revista:
Biochim Biophys Acta Rev Cancer
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos