Author

Yuepeng Jiang

City University of Hong KongORCID 0000-0002-1444-7183Unclaimed 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· 4 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Assay:protein sequence· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Shuai Cheng LiiD· 3 shared Miaozhe HuoiD· 2 shared Pingping ZhangiD· 1 shared Russ Biagio AltmaniD· 1 shared Sheng WangiD· 1 shared Stefano RensiiD· 1 shared Yiping ZouiD· 1 shared

Activity by Year

2020-2023 · peak 3/yr

Co-Author Collaboration Network

7 top collaborators · 9 mutual co-author links

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3 shared papersBioinformatics

Shuai Cheng LiORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

TCR2vec: a deep representation learning framework of T-cell receptor sequence and function

Yuepeng Jiang, Miaozhe Huo, Pingping Zhang, Yiping Zou, Shuai Cheng Li

TCR2vec is a deep learning framework that learns representations of full T-cell receptors, outperforming methods that focus only on CDR3 regions in predicting binding specificity and clustering.

bioRxiv · Mar 31, 2023 · PreprintBioinformatics

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity

Yuepeng Jiang, Miaozhe Huo, Shuai Cheng Li

TEINet predicts TCR-epitope binding specificity using a deep learning framework with transfer learning, achieving an AUROC of 0.760 and outperforming baselines.

Briefings in Bioinformatics · Feb 15, 2023Peer-reviewedBioinformatics

Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires

Yuepeng Jiang, Shuai Cheng Li

This paper introduces TCRpeg, a deep autoregressive generative model for TCR repertoires, achieving higher accuracy in probability estimation and improving multiple TCR-related tasks.

Briefings in Bioinformatics · Jan 19, 2023Peer-reviewedBioinformatics

DrugOrchestra: Jointly predicting drug response, targets, and side effects via deep multi-task learning

Yuepeng Jiang, Stefano Rensi, Sheng Wang, Russ Biagio Altman

DrugOrchestra jointly predicts drug response, targets, and side effects using deep multi-task learning, showing improved performance over single-task methods.

bioRxiv · Nov 17, 2020 · PreprintBioinformatics