Computational methods for Gene Regulatory Networks reconstruction and analysis: A review

•The inference of Gene Regulatory Networks enables the integrative analysis of biological systems.•Novel computational approaches have been developed for network reconstruction and evaluation.•Gene Network validation still remains a challenging step of the process.•This review presents a comprehensi...

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Veröffentlicht in:Artificial intelligence in medicine Jg. 95; S. 133 - 145
Hauptverfasser: Delgado, Fernando M., Gómez-Vela, Francisco
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.04.2019
Schlagworte:
Computational Biology - methods
Gene Network
Gene Network inference
Gene Regulatory Network
Gene Regulatory Networks
Humans
Networks validation
Systems biology
Networks validation
Gene Network inference
Gene Regulatory Network
Systems biology
Gene Network
ISSN:0933-3657, 1873-2860, 1873-2860
Online-Zugang:Volltext
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Zusammenfassung:•The inference of Gene Regulatory Networks enables the integrative analysis of biological systems.•Novel computational approaches have been developed for network reconstruction and evaluation.•Gene Network validation still remains a challenging step of the process.•This review presents a comprehensive review of the field. In the recent years, the vast amount of genetic information generated by new-generation approaches, have led to the need of new data handling methods. The integrative analysis of diverse-nature gene information could provide a much-sought overview to study complex biological systems and processes. In this sense, Gene Regulatory Networks (GRN) arise as an increasingly-promising tool for the modelling and analysis of biological processes. This review is an attempt to summarize the state of the art in the field of GRNs. Essential points in the field are addressed, thereof: (a) the type of data used for network generation, (b) machine learning methods and tools used for network generation, (c) model optimization and (d) computational approaches used for network validation. This survey is intended to provide an overview of the subject for readers to improve their knowledge in the field of GRN for future research.
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ISSN:0933-3657
1873-2860
1873-2860
DOI:10.1016/j.artmed.2018.10.006