Author

Jinyu Yang

The University of Texas at ArlingtonORCID 0000-0002-7004-3570Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Paleontology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Assay:ChIP-seq· 1 Research Focus:deconvolution· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Adam D. HoppeiD· 1 shared Anjun MaiD· 1 shared Bingqiang LiuiD· 1 shared Cankun WangiD· 1 shared Chi ZhangiD· 1 shared Chunyan Zhou· 1 shared Fei Liang· 1 shared Gangqiang Cao· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBioinformatics

Adam D. HoppeORCID

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2 papers

Recent·Most cited

Unveiling the Mystery of the Petroleum Genome Archive: Paleofauna Activities, Geological History, and aDNA Identification Methods in Nanyang Oilfield

Wan-Qian Zhao, Zhanyong Guo, Yuqi Guo, Meijun Li, Gangqiang Cao, Zengyuan Tian, Ran Chai, Liyou Qiu, Jin-Hua Zeng, Xin-Ge Zhang, Tian-Cang Qin, Jinyu Yang, Mingjie Chen, Meirong Song, Fei Liang, , Lijuan Zhao

This study extracts and classifies DNA fragments from petroleum samples, revealing genetic insights into ancient environments and human history, while challenging traditional aDNA methods.

bioRxiv · Dec 24, 2024 · PreprintPaleontology

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework

Jinyu Yang, Anjun Ma, Adam D. Hoppe, Cankun Wang, Yang Li, Chi Zhang, Yan Wang, Bingqiang Liu, Qin Ma

The paper introduces DESSO, a deep learning framework for predicting regulatory motifs from ChIP-Seq data, showing improved accuracy over existing tools.

Nucleic Acids Research · Jul 24, 2019Peer-reviewedBioinformatics