DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features-Reference-Cited by-同舟云学术

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DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features

Published:2023-12-13 Issue: Volume: Page:
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Xiang Xiaoyang¹,Gao Jiaxuan¹,Ding Yanrui¹^ORCID

Affiliation:

1. School of Science, Jiangnan University, Wuxi, P. R. China.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2023.0097

Reference41 articles.

1. A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins

2. Unified rational protein engineering with sequence-based deep representation learning

3. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

4. A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

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