| Title: | Toward learning the principles of plant gene regulation |
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| Authors: | ID Zrimec, Jan (Author)
ID Zelezniak, Aleksej (Author)
ID Gruden, Kristina (Author) |
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| Files: |  URL - Source URL, visit https://doi.org/10.1016/j.tplants.2022.08.010
 PDF - Presentation file, download (403,50 KB)
MD5: 27642738E5CF5970A22FF390D0382930
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| Language: | English |
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| Typology: | 1.03 - Other scientific articles |
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| Organization: |  NIB - National Institute of Biology
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| Abstract: | Advanced machine learning (ML) algorithms produce highly accurate models of gene expression, uncovering novel regulatory features in nucleotide sequences involving multiple cis-regulatory regions across whole genes and structural properties. These broaden our understanding of gene regulation and point to new principles to test and adopt in the field of plant science.
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| Keywords: | gene expression prediction, bioinformatics, deep learning, regulatory genomics |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 01.12.2022 |
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| Year of publishing: | 2022 |
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| Number of pages: | str. 1206-1208 |
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| Numbering: | Vol. 27, iss. 12 |
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| PID: | 20.500.12556/DiRROS-20157  |
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| UDC: | 60 |
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| ISSN on article: | 1360-1385 |
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| DOI: | 10.1016/j.tplants.2022.08.010 |
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| COBISS.SI-ID: | 123482371 |
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| Publication date in DiRROS: | 06.08.2024 |
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| Views: | 290 |
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| Downloads: | 130 |
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| Metadata: |    |
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