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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24882
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dc.contributor.authorZrimec, Jan-
dc.contributor.authorBuric, Filip-
dc.contributor.authorKokina, Mariia-
dc.contributor.authorGarcia, Victor-
dc.contributor.authorZelezniak, Aleksej-
dc.date.accessioned2022-04-28T09:31:35Z-
dc.date.available2022-04-28T09:31:35Z-
dc.date.issued2021-06-
dc.identifier.issn2296-889Xde_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24882-
dc.description.abstractData-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.de_CH
dc.language.isoende_CH
dc.publisherFrontiers Research Foundationde_CH
dc.relation.ispartofFrontiers in Molecular Biosciencesde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectChromatin accessibilityde_CH
dc.subjectCis-regulatory grammarde_CH
dc.subjectDeep neural networkde_CH
dc.subjectGene expression predictionde_CH
dc.subjectGene regulatory structurede_CH
dc.subjectmRNA & protein abundancede_CH
dc.subjectMachine learningde_CH
dc.subjectRegulatory genomicsde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc572: Biochemiede_CH
dc.titleLearning the regulatory code of gene expressionde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.3389/fmolb.2021.673363de_CH
dc.identifier.doi10.21256/zhaw-24882-
dc.identifier.pmid34179082de_CH
zhaw.funding.euNode_CH
zhaw.issue673363de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume8de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf182330de_CH
zhaw.webfeedApplied Mathematical Biologyde_CH
zhaw.webfeedDIZH Fellowshipde_CH
zhaw.funding.zhawDigitale Werkzeuge zur Codonoptimierungde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Zrimec, J., Buric, F., Kokina, M., Garcia, V., & Zelezniak, A. (2021). Learning the regulatory code of gene expression. Frontiers in Molecular Biosciences, 8(673363). https://doi.org/10.3389/fmolb.2021.673363
Zrimec, J. et al. (2021) ‘Learning the regulatory code of gene expression’, Frontiers in Molecular Biosciences, 8(673363). Available at: https://doi.org/10.3389/fmolb.2021.673363.
J. Zrimec, F. Buric, M. Kokina, V. Garcia, and A. Zelezniak, “Learning the regulatory code of gene expression,” Frontiers in Molecular Biosciences, vol. 8, no. 673363, Jun. 2021, doi: 10.3389/fmolb.2021.673363.
ZRIMEC, Jan, Filip BURIC, Mariia KOKINA, Victor GARCIA und Aleksej ZELEZNIAK, 2021. Learning the regulatory code of gene expression. Frontiers in Molecular Biosciences. Juni 2021. Bd. 8, Nr. 673363. DOI 10.3389/fmolb.2021.673363
Zrimec, Jan, Filip Buric, Mariia Kokina, Victor Garcia, and Aleksej Zelezniak. 2021. “Learning the Regulatory Code of Gene Expression.” Frontiers in Molecular Biosciences 8 (673363). https://doi.org/10.3389/fmolb.2021.673363.
Zrimec, Jan, et al. “Learning the Regulatory Code of Gene Expression.” Frontiers in Molecular Biosciences, vol. 8, no. 673363, June 2021, https://doi.org/10.3389/fmolb.2021.673363.


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