NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool-Reference-Cited by-同舟云学术

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NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool

Published:2023-05-30 Issue:23 Volume:8 Page:20379-20388
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Madugula Sita Sirisha¹,Pandey Suman¹,Amalapurapu Shreya¹²,Bozdag Serdar¹³⁴^ORCID

Affiliation:

1. Department of Computer Science & Engineering, University of North Texas, Denton, Texas TX 76203, United States

2. The Texas Academy of Mathematics and Science, University of North Texas, Denton, Texas TX 76203, United States

3. Department of Mathematics, University of North Texas, Denton, Texas TX 76203, United States

4. BioDiscovery Institute, University of North Texas, Denton, Texas TX 76203, United States

Funder

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c00286

Reference53 articles.

1. The nuclear receptor superfamily: The second decade

2. Mechanism of the nuclear receptor molecular switch

3. Principles for modulation of the nuclear receptor superfamily

4. The Structural Basis of Estrogen Receptor/Coactivator Recognition and the Antagonism of This Interaction by Tamoxifen

5. The nuclear receptor superfamily: A structural perspective

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1. ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms;Biochimica et Biophysica Acta (BBA) - General Subjects;2024-06

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