Author

Ye Wang

Indiana University IndianapolisORCID 0000-0002-0123-1260Unclaimed ORCID profile · follow to get notified of new preprints

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Ye Wang3 papers · Columbia University

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Approach:large language model· 1 Research Focus:software / tool· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

ADVANCED Working Group· 1 shared Bingyi WangiD· 1 shared Di Zhu· 1 shared Dongrui Peng· 1 shared Fan WangiD· 1 shared Fengdi ZhaoiD· 1 shared Haodong LiiD· 1 shared Honghao LaiiD· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperBioinformatics

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2 papers

Recent·Most cited

BioinformaticsJournal of the American Medical Informatics AssociationPeer-reviewedMay 27, 2025

RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels

Zeming Li, Xufei Luo, Zhenhua Yang…·Shilin Tang

RAPID is an automated tool using large language models to generate medical reporting checklists, achieving high accuracy on CONSORT and CONSORT-AI datasets.

7 citationstop 4%

large language modelsoftware / tool

𝕏 PostBluesky

BioinformaticsBioinformaticsPeer-reviewedApr 7, 2022

Exploiting deep transfer learning for the prediction of functional noncoding variants using genomic sequence

Li Chen, Ye Wang, Fengdi Zhao

The paper uses deep transfer learning to predict functional noncoding variants, outperforming existing methods on MPRA and GWAS datasets.

21 citations

whole-genome sequencingdeep learning

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