Author

Hunyong Cho

University of North Carolina at Chapel HillORCID 0000-0002-0087-0079Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:differential expression· 2 Assay:metagenomics· 2 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Research Focus:gene regulatory network inference· 1 Assay:metabolomics· 1

Frequent Co-Authors

Di WuiD· 3 shared Apoena Aguiar RibeiroiD· 2 shared Bridget M. LiniD· 2 shared Chengwen LiiD· 2 shared Jeffrey M. RoachiD· 2 shared Kimon DivarisiD· 2 shared Boyang Tang· 1 shared Chuwen LiuiD· 1 shared

Activity by Year

2023-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersBioinformatics

Di WuORCID

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Author profile →

3 papers

Recent·Most cited

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data

Hunyong Cho, Yixiang Qu, Chengwen Li, Boyang Tang, Ruiqi Lyu, Bridget M. Lin, Jeffrey M. Roach, M. Andrea Azcárate-Peril, Apoena Aguiar Ribeiro, Michael I. Love, Kimon Divaris, Di Wu

The paper evaluates ten differential expression methods for metatranscriptomics data using real and simulated datasets, finding the logistic-beta test has highest sensitivity and reasonable type I error control.

Briefings in Bioinformatics · Jul 21, 2023Peer-reviewedBioinformatics

A bivariate zero-inflated negative binomial model and its applications to biomedical settings

Hunyong Cho, Chengwen Li, John S. Preisser, Di Wu

The paper develops a bivariate zero-inflated negative binomial model and applies it to analyze zero-inflated count data from scRNA-seq and dental caries studies.

Statistical Methods in Medical Research · May 11, 2023Peer-reviewedGenomics

BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data

Bridget M. Lin, Hunyong Cho, Chuwen Liu, Jeffrey M. Roach, Apoena Aguiar Ribeiro, Kimon Divaris, Di Wu

The paper introduces a BZINB model-based method for integrating microbiome and metabolome data, showing improved accuracy in identifying correlations compared to traditional methods.

bioRxiv · Jan 30, 2023 · PreprintBioinformatics