Detailed Information

Cited 0 time in webofscience Cited 1 time in scopus
Metadata Downloads

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

Authors
Song, Jun-TaeWoo, Dong-ULee, YejinChoi, Sung-HoonKang, Yang-Jae
Issue Date
Oct-2021
Publisher
MDPI
Keywords
resveratrol synthesis; machine learning; gene family expansion; synthetic biotechnology
Citation
PLANTS-BASEL, v.10, no.10
Indexed
SCIE
SCOPUS
Journal Title
PLANTS-BASEL
Volume
10
Number
10
URI
https://scholarworks.bwise.kr/gnu/handle/sw.gnu/3193
DOI
10.3390/plants10102058
ISSN
2223-7747
Abstract
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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
자연과학대학 > Division of Life Sciences > Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kang, Yang Jae photo

Kang, Yang Jae
자연과학대학 (생명과학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE