FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework-Reference-Cited by-同舟云学术

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework

Published:2024-02-13 Issue:4 Volume:64 Page:1394-1406
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zhang Ming¹,Gong Chao¹^ORCID,Ge Fang²,Yu Dong-Jun³^ORCID

Affiliation:

1. School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China

2. State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China

3. School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China

Funder

Natural Science Foundation of Jiangsu Province

Nanjing University of Posts and Telecommunications

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c02025

Reference47 articles.

1. A review study: Computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases

2. STRUM: structure-based prediction of protein stability changes upon single-point mutation

3. Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes

4. A Bioinformatics Workflow for Variant Peptide Detection in Shotgun Proteomics

5. DAMpred: Recognizing Disease-Associated nsSNPs through Bayes-Guided Neural-Network Model Built on Low-Resolution Structure Prediction of Proteins and Protein–Protein Interactions

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