From time-series transcriptomics to gene regulatory networks: A review on inference methods.
PLoS Comput Biol
; 19(8): e1011254, 2023 08.
Article
em En
| MEDLINE
| ID: mdl-37561790
ABSTRACT
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Redes Reguladoras de Genes
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Transcriptoma
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
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INFORMATICA MEDICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
França