Author

Ying He

Tongji UniversityORCID 0000-0002-9592-7727Unclaimed ORCID profile · follow to get notified of new preprints

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Ying He4 papers · Columbia University

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Assay:ATAC-seq· 1 Research Focus:benchmarking· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

De-Shuang HuangiD· 2 shared Qi LiuiD· 2 shared Qinhu ZhangiD· 2 shared Siguo WangiD· 2 shared Zhen CuiiD· 2 shared Changan YuaniD· 1 shared Pengrui TengiD· 1 shared Yixin LiuiD· 1 shared

Activity by Year

2022-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersBioinformatics

De-Shuang HuangORCID

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2 papers

Recent·Most cited

Computational prediction and characterization of cell-type-specific and shared binding sites

Qinhu Zhang, Pengrui Teng, Siguo Wang, Ying He, Zhen Cui, Zhenghao Guo, Yixin Liu, Changan Yuan, Qi Liu, De-Shuang Huang

The paper proposes two computational methods to predict and characterize cell-type-specific and shared TF binding sites using XGBoost and CNN, showing improved performance over existing methods.

Bioinformatics · Dec 9, 2022Peer-reviewedBioinformatics

Base-resolution prediction of transcription factor binding signals by a deep learning framework

Qinhu Zhang, Ying He, Siguo Wang, Zhan‐Heng Chen, Zhenhao Guo, Zhen Cui, Qi Liu, De-Shuang Huang

The paper introduces a deep learning framework called FCNsignal to predict TF-DNA binding signals at base-resolution, outperforming existing methods and demonstrating its ability to locate binding regions and predict motifs.

PLoS Computational Biology · Mar 9, 2022Peer-reviewedBioinformatics