A guide to machine learning for biologists.
Nat Rev Mol Cell Biol
; 23(1): 40-55, 2022 01.
Article
en En
| MEDLINE
| ID: mdl-34518686
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Biología
/
Aprendizaje Automático
Tipo de estudio:
Guideline
/
Prognostic_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Nat Rev Mol Cell Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2022
Tipo del documento:
Article