Author

Ke Shen

Peking UniversityORCID 0000-0001-5133-9248Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 2 Approach:deep learning· 2 Assay:DNA methylation· 2 Research Focus:gene regulatory network inference· 2 Research Focus:variant effect prediction· 2 Research Focus:differential expression· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Xiaoliang Sunney XieiD· 2 shared Zi WangiD· 2 shared Chen Xie· 1 shared Di Wang· 1 shared Dubai LiiD· 1 shared Jian YaniD· 1 shared Jun ReniD· 1 shared Junchen Luo· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBiophysics

Xiaoliang Sunney XieORCID

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2 papers

Recent·Most cited

BiophysicsbioRxivPreprintMay 20, 2026

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Zi Wang, Di Wang, Ke Shen…·Xiaoliang Sunney Xie

The paper introduces Seq2Kd, a deep learning model predicting TF-DNA binding affinity using high-throughput Kd measurements, and provides a web portal for functional genomics and disease research.

DOI 10.64898/2026.05.18.725930

humanDNA methylation

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BiophysicsbioRxivPreprintMay 19, 2026

Deep Learning of High-throughput Transcription Factor-DNA Binding Affinity Data: Quantitative Comparison with Pairwise-Additive Models

Ke Shen, Zi Wang, Xiaoliang Sunney Xie

The paper compares deep learning models with pairwise-additive models for predicting TF-DNA binding affinity using high-throughput experimental data, showing improved accuracy with non-additive models.

1 citation

DNA methylationChIP-seq

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