Author

Yu Shi

University of Nebraska–LincolnORCID 0000-0003-1272-8392Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Yu Shi3 papers · National University of Singapore

Biology Disciplines

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Bioinformatics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:GWAS / association mapping· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Chi ZhangiD· 3 shared Ahsan KamaliD· 1 shared Alyssa J. Cai· 1 shared Carolyn Xie· 1 shared Dandan ZhengiD· 1 shared Haishuang LiniD· 1 shared Hongfeng YuiD· 1 shared Huijing Du· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

Chi ZhangORCID

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3 papers

Recent·Most cited

Genetic & Genomic MedicinemedRxivPreprintNov 2, 2023

radioGWAS: link radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer

Dandan Zheng, Paul M. Grandgenett, Qi Zhang…·Chi Zhang

This study links radiomic features to genome-wide associations in pancreatic cancer to identify driver genes associated with tumor structural heterogeneity and patient survival.

1 citation

humanbulk RNA-seq

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BioinformaticsbioRxivPreprintOct 21, 2022

Identification of RNA-based cell-type markers for the stem cell manufacture system with a statistical scoring function

Yu Shi, Weilong Yang, Haishuang Lin…·Chi Zhang

The paper identifies RNA-based cell-type markers for stem cell manufacture using a statistical scoring function and validates them with deep learning and simulated data.

DOI 10.1101/2022.10.21.513209

humanbulk RNA-seq

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BioinformaticsbioRxivPreprintNov 10, 2021

Deeplearning based MHC epitope prediction for cancer neoantigen discovery

Carolyn Xie, Yu Shi, Chi Zhang

This paper uses XGBoost with tri-peptide similarity to predict MHC epitopes for cancer neoantigen discovery, tested on experimentally validated peptides.

2 citations

machine learning (classical)

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