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The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis

Resveratrol is a phytochemical with medicinal benefits, being well-known for its presence in wine. Plants develop resveratrol in response to stresses such as pathogen infection, UV radiation, and other mechanical stress. The recent publications of genomic sequences of resveratrol-producing plants su...

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Detalles Bibliográficos
Autores principales: Song, Jun-Tae, Woo, Dong-U, Lee, Yejin, Choi, Sung-Hoon, Kang, Yang-Jae
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538884/
https://www.ncbi.nlm.nih.gov/pubmed/34685867
http://dx.doi.org/10.3390/plants10102058
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author Song, Jun-Tae
Woo, Dong-U
Lee, Yejin
Choi, Sung-Hoon
Kang, Yang-Jae
author_facet Song, Jun-Tae
Woo, Dong-U
Lee, Yejin
Choi, Sung-Hoon
Kang, Yang-Jae
author_sort Song, Jun-Tae
collection PubMed
description Resveratrol is a phytochemical with medicinal benefits, being well-known for its presence in wine. Plants develop resveratrol in response to stresses such as pathogen infection, UV radiation, and other mechanical stress. The recent publications of genomic sequences of resveratrol-producing plants such as grape, peanut, and eucalyptus can expand our molecular understanding of resveratrol synthesis. Based on a gene family count matrix of Viridiplantae members, we uncovered important gene families that are common in resveratrol-producing plants. These gene families could be prospective candidates for improving the efficiency of synthetic biotechnology-based artificial resveratrol manufacturing.
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spelling pubmed-85388842021-10-24 The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis Song, Jun-Tae Woo, Dong-U Lee, Yejin Choi, Sung-Hoon Kang, Yang-Jae Plants (Basel) Communication Resveratrol is a phytochemical with medicinal benefits, being well-known for its presence in wine. Plants develop resveratrol in response to stresses such as pathogen infection, UV radiation, and other mechanical stress. The recent publications of genomic sequences of resveratrol-producing plants such as grape, peanut, and eucalyptus can expand our molecular understanding of resveratrol synthesis. Based on a gene family count matrix of Viridiplantae members, we uncovered important gene families that are common in resveratrol-producing plants. These gene families could be prospective candidates for improving the efficiency of synthetic biotechnology-based artificial resveratrol manufacturing. MDPI 2021-09-29 /pmc/articles/PMC8538884/ /pubmed/34685867 http://dx.doi.org/10.3390/plants10102058 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Song, Jun-Tae
Woo, Dong-U
Lee, Yejin
Choi, Sung-Hoon
Kang, Yang-Jae
The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis
title The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis
title_full The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis
title_fullStr The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis
title_full_unstemmed The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis
title_short The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis
title_sort semi-supervised strategy of machine learning on the gene family diversity to unravel resveratrol synthesis
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538884/
https://www.ncbi.nlm.nih.gov/pubmed/34685867
http://dx.doi.org/10.3390/plants10102058
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