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  Supervised Learning of Gene Regulatory Networks

Razaghi-Moghadam, Z., & Nikoloski, Z. (2020). Supervised Learning of Gene Regulatory Networks. Current Protocols in Plant Biology, 5(2): e20106. doi:10.1002/cppb.20106.

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Razaghi-Moghadam, Z.1, Author              
Nikoloski, Z.1, Author              
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1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              

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Free keywords: gene expression profiles, gene regulatory networks, supervised learning, support vector machine
 Abstract: Abstract 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

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Language(s): eng - English
 Dates: 2020
 Publication Status: Published in print
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 Identifiers: DOI: 10.1002/cppb.20106
BibTex Citekey: doi:10.1002/cppb.20106
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Title: Current Protocols in Plant Biology
Source Genre: Journal
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Pages: - Volume / Issue: 5 (2) Sequence Number: e20106 Start / End Page: - Identifier: -