Supervised learning of gene regulatory networks
- 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?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.…
Author details: | Zahra Razaghi-MoghadamORCiD, Zoran NikoloskiORCiDGND |
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URN: | urn:nbn:de:kobv:517-opus4-516561 |
DOI: | https://doi.org/10.25932/publishup-51656 |
ISSN: | 1866-8372 |
Title of parent work (German): | Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe |
Publication series (Volume number): | Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (1185) |
Publication type: | Postprint |
Language: | English |
Date of first publication: | 2021/11/02 |
Publication year: | 2020 |
Publishing institution: | Universität Potsdam |
Release date: | 2021/11/02 |
Tag: | gene expression profiles; gene regulatory networks; supervised learning; support vector machine |
Article number: | e20106 |
Number of pages: | 9 |
Source: | Current Protocols in Plant Biology, 5 (2020) e20106 DOI: 10.1002/cppb.20106 |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 58 Pflanzen (Botanik) / 580 Pflanzen (Botanik) |
Peer review: | Referiert |
Publishing method: | Open Access / Green Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |