Author

Yuxin Cui

University of South CarolinaORCID 0000-0002-5904-4169Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Organism:human· 3 Research Focus:differential expression· 1 Assay:protein sequence· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Alireza NasiriiD· 3 shared Jianjun HuiD· 3 shared Zhonghao LiuiD· 3 shared Ansi ZhangiD· 2 shared Jing JiniD· 2 shared Yong ZhaoiD· 2 shared Zheng XiongiD· 2 shared Stephen Louis· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Alireza NasiriORCID

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3 papers

Recent·Most cited

BioinformaticsIEEE Transactions on Computational Biology and BioinformaticsPeer-reviewedApr 22, 2021

DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction

Zhonghao Liu, Jing Jin, Yuxin Cui…·Jianjun Hu

DeepSeqPanII predicts peptide-HLA class II binding using an interpretable recurrent neural network with attention mechanisms, achieving state-of-the-art performance.

47 citations

humandeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintNov 7, 2019

Attention mechanism-based deep learning pan-specificmodel for interpretable MHC-I peptide binding prediction

Jing Jin, Zhonghao Liu, Alireza Nasiri…·Jianjun Hu

The paper introduces DeepAttentionPan, a deep learning model for MHC-I peptide binding prediction that uses attention mechanisms to improve interpretability and performance.

4 citations

humandeep learning

𝕏 PostBluesky

BioinformaticsScientific ReportsPeer-reviewedJan 28, 2019

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Zhonghao Liu, Yuxin Cui, Zheng Xiong…·Jianjun Hu

DeepSeqPan is a deep convolutional neural network for predicting HLA-peptide binding affinity without requiring structural information, achieving state-of-the-art performance on multiple HLA alleles.

60 citations

humanprotein sequence

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