| Title: | Toward learning the principles of plant gene regulation |
|---|
| Authors: | ID Zrimec, Jan (Author)
ID Zelezniak, Aleksej (Author)
ID Gruden, Kristina (Author) |
|---|
| Files: |  URL - Source URL, visit https://doi.org/10.1016/j.tplants.2022.08.010
 PDF - Presentation file, download (403,50 KB)
MD5: 27642738E5CF5970A22FF390D0382930
|
|---|
| Language: | English |
|---|
| Typology: | 1.03 - Other scientific articles |
|---|
| Organization: |  NIB - National Institute of Biology
|
|---|
| 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.
|
|---|
| Keywords: | gene expression prediction, bioinformatics, deep learning, regulatory genomics |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Publication date: | 01.12.2022 |
|---|
| Year of publishing: | 2022 |
|---|
| Number of pages: | str. 1206-1208 |
|---|
| Numbering: | Vol. 27, iss. 12 |
|---|
| PID: | 20.500.12556/DiRROS-20157  |
|---|
| UDC: | 60 |
|---|
| ISSN on article: | 1360-1385 |
|---|
| DOI: | 10.1016/j.tplants.2022.08.010 |
|---|
| COBISS.SI-ID: | 123482371 |
|---|
| Publication date in DiRROS: | 06.08.2024 |
|---|
| Views: | 958 |
|---|
| Downloads: | 437 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
