Supervised Learning of Gene Regulatory Networks.
gene expression profiles
gene regulatory networks
supervised learning
support vector machine
Journal
Current protocols in plant biology
ISSN: 2379-8068
Titre abrégé: Curr Protoc Plant Biol
Pays: United States
ID NLM: 101685882
Informations de publication
Date de publication:
06 2020
06 2020
Historique:
entrez:
25
3
2020
pubmed:
25
3
2020
medline:
4
9
2020
Statut:
ppublish
Résumé
Identifying the entirety of gene regulatory interactions in a biological system offers the possibility to determine the key molecular factors that affect important traits on the level of cells, tissues, and whole organisms. Despite the development of experimental approaches and technologies for identification of direct binding of transcription factors (TFs) to promoter regions of downstream target genes, computational approaches that utilize large compendia of transcriptomics data are still the predominant methods used to predict direct downstream targets of TFs, and thus reconstruct genome-wide gene-regulatory networks (GRNs). These approaches can broadly be categorized into unsupervised and supervised, based on whether data about known, experimentally verified gene-regulatory interactions are used in the process of reconstructing the underlying GRN. Here, we first describe the generic steps of supervised approaches for GRN reconstruction, since they have been recently shown to result in improved accuracy of the resulting networks? We also illustrate how they can be used with data from model organisms to obtain more accurate prediction of gene regulatory interactions. © 2020 The Authors. Basic Protocol 1: Construction of features used in supervised learning of gene regulatory interactions Basic Protocol 2: Learning the non-interacting TF-gene pairs Basic Protocol 3: Learning a classifier for gene regulatory interactions.
Substances chimiques
Transcription Factors
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e20106Informations de copyright
© 2020 The Authors.
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