Cargando…
Machine learning models reveal the importance of time-point specific cis-regulatory elements in Arabidopsis thaliana wounding response
| Autor principal: | |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2021
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8824588/ https://www.ncbi.nlm.nih.gov/pubmed/35231110 http://dx.doi.org/10.1093/plcell/koab288 |
| _version_ | 1784647041944649728 |
|---|---|
| author | Kenchanmane Raju, Sunil Kumar |
| author_facet | Kenchanmane Raju, Sunil Kumar |
| author_sort | Kenchanmane Raju, Sunil Kumar |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-8824588 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | Oxford University Press |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-88245882022-02-09 Machine learning models reveal the importance of time-point specific cis-regulatory elements in Arabidopsis thaliana wounding response Kenchanmane Raju, Sunil Kumar Plant Cell In Brief Oxford University Press 2021-12-07 /pmc/articles/PMC8824588/ /pubmed/35231110 http://dx.doi.org/10.1093/plcell/koab288 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| spellingShingle | In Brief Kenchanmane Raju, Sunil Kumar Machine learning models reveal the importance of time-point specific cis-regulatory elements in Arabidopsis thaliana wounding response |
| title | Machine learning models reveal the importance of time-point specific cis-regulatory elements in Arabidopsis thaliana wounding response |
| title_full | Machine learning models reveal the importance of time-point specific cis-regulatory elements in Arabidopsis thaliana wounding response |
| title_fullStr | Machine learning models reveal the importance of time-point specific cis-regulatory elements in Arabidopsis thaliana wounding response |
| title_full_unstemmed | Machine learning models reveal the importance of time-point specific cis-regulatory elements in Arabidopsis thaliana wounding response |
| title_short | Machine learning models reveal the importance of time-point specific cis-regulatory elements in Arabidopsis thaliana wounding response |
| title_sort | machine learning models reveal the importance of time-point specific cis-regulatory elements in arabidopsis thaliana wounding response |
| topic | In Brief |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8824588/ https://www.ncbi.nlm.nih.gov/pubmed/35231110 http://dx.doi.org/10.1093/plcell/koab288 |
| work_keys_str_mv | AT kenchanmanerajusunilkumar machinelearningmodelsrevealtheimportanceoftimepointspecificcisregulatoryelementsinarabidopsisthalianawoundingresponse |