Toward learning the principles of plant gene regulation.

Genes, Plant Gene Expression Regulation, Plant / genetics Machine Learning Algorithms Regulatory Sequences, Nucleic Acid
DNA motifs DNA regulatory code deep neural networks gene expression prediction gene regulatory structure machine learning

Journal

Trends in plant science
ISSN: 1878-4372
Titre abrégé: Trends Plant Sci
Pays: England
ID NLM: 9890299

Informations de publication

Date de publication:
12 2022
Historique:
received: 09 04 2022
revised: 09 08 2022
accepted: 17 08 2022
pubmed: 14 9 2022
medline: 15 11 2022
entrez: 13 9 2022
Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1016/j.tplants.2022.08.010 PMID: 36100536
pubmed: 36100536
pii: S1360-1385(22)00216-3
doi: 10.1016/j.tplants.2022.08.010
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1206-1208

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests No interests are declared.

Auteurs

Jan Zrimec (J)

Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000 Ljubljana, Slovenia. Electronic address: jan.zrimec@nib.si.

Aleksej Zelezniak (A)

Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, 412 96, Gothenburg, Sweden.

Kristina Gruden (K)

Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000 Ljubljana, Slovenia. Electronic address: kristina.gruden@nib.si.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Multiple NADPH-cytochrome P450 reductases from Lycoris radiata involved in Amaryllidaceae alkaloids biosynthesis.

Yuqing Wu, Yifeng Zhang, Haitong Fan et al.
1.00
Amaryllidaceae Alkaloids Lycoris NADPH-Ferrihemoprotein Reductase Gene Expression Regulation, Plant Plant Proteins

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software

Systematical characterization of Rab7 gene family in Gossypium and potential functions of GhRab7B3-A gene in drought tolerance.

Mengyuan Yan, Zhiwei Dong, Tian Pan et al.
1.00
Drought Resistance Gene Expression Profiling Gene Expression Regulation, Plant Gossypium Multigene Family

Classifications MeSH

questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Génome › Génome végétal › Gènes de plante › Toward learning the principles of plant gene...
questionsmedicales.fr › Phénomènes génétiques › Régulation de l'expression des gènes › Régulation de l'expression des gènes végétaux › Toward learning the principles of plant gene...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Toward learning the principles of plant gene...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Toward learning the principles of plant gene...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Séquences d'acides nucléiques régulatrices › Toward learning the principles of plant gene...