Editorial: Modern machine learning approaches for quantitative inference of gene regulation from genomic and epigenomic features

Michael Banf; Kangmei Zhao; Thomas Hartwig

doi:10.3389/fpls.2023.1330793

Editorial: Modern machine learning approaches for quantitative inference of gene regulation from genomic and epigenomic features

Front Plant Sci. 2023 Nov 24:14:1330793. doi: 10.3389/fpls.2023.1330793. eCollection 2023.

Authors

Michael Banf^{1

2}, Kangmei Zhao³, Thomas Hartwig⁴

Affiliations

¹ Perelyn, Munich, Germany.
² EducatedGuess.ai, Siegen, Germany.
³ Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, United States.
⁴ Institute for Molecular Physiology, Cluster of Excellence on Plant Sciences, Heinrich-Heine University, Düsseldorf, Germany.

No abstract available

Keywords: epigenomics; gene expression; gene regulation; generative modeling; genomic foundation models; genomics; machine learning; transcriptional regulation.

Publication types

Editorial