Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.

导出 OA

翻译打印收藏纠错

第一作者： Mohammad,Madani

第一单位： Department of Mechanical Engineering, University of Connecticut, Storrs, CT, United States.;Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, United States.

作者： Mohammad,Madani ^[1] ; Mohammad,Mahdi Behzadi ^[1] ; Dongjin,Song ^[2] ; Horea T,Ilies ^[3] ; Anna,Tarakanova ^[4]

作者单位： Department of Mechanical Engineering, University of Connecticut, Storrs, CT, United States.;Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, United States. ^[1] Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, United States. ^[2] Department of Mechanical Engineering, University of Connecticut, Storrs, CT, United States. ^[3] Department of Mechanical Engineering, University of Connecticut, Storrs, CT, United States.;Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States. ^[4]

关键词 AF2, AlphaFold2 CASP CGAN-Cmap GAN GANs, Generative adversarial neural networks MSA Protein contact map ResNet, Residual neural network SE-Concat

DOI 10.1016/j.csbj.2022.11.020

PMID 36420166

发布时间 2022-11-25

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

Computational and structural biotechnology journal

2022年20卷

6138-6148页

相似文献

中文期刊
外文期刊
学位论文
会议论文

检索历史清除

Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.

Computational and structural biotechnology journal

相似文献

Computational and structural biotechnology journal

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.

Computational and structural biotechnology journal

相似文献

Computational and structural biotechnology journal

相关期刊

检索历史清除