ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms-Reference-Cited by-同舟云学术

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ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms

Published:2024-06 Issue:6 Volume:1868 Page:130597
ISSN:0304-4165
Container-title:Biochimica et Biophysica Acta (BBA) - General Subjects
language:en
Short-container-title:Biochimica et Biophysica Acta (BBA) - General Subjects

Author:

Pradhan Upendra Kumar,Mahapatra Anuradha,Naha Sanchita,Gupta Ajit,Parsad Rajender,Gahlaut Vijay,Rath Surya Narayan,Meher Prabina Kumar

Funder

ICAR-Indian Agricultural Statistics Research Institute

Publisher

Elsevier BV

Reference77 articles.

1. Unlocking triticeae genomics to sustainably feed the future;Mochida;Plant Cell Physiol.,2013

2. Plant productivity and environment;Boyer;Science,1982

3. Emerging trends in the functional genomics of the abiotic stress response in crop plants;Vij;Plant Biotechnol. J.,2007

4. Engineering the future. Development of transgenic plants with enhanced tolerance to adverse environments;Zurbriggen;Biotechnol. Genet. Eng. Rev.,2010

5. 2 - effect of climate change on agricultural crops;Mall,2017

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Epigenetics in regulating plant responses to growth and environmental cues;Biochimica et Biophysica Acta (BBA) - General Subjects;2024-08

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