Author

Hongjie Ke

University of Maryland, College Park

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:Bayesian inference· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1 Organism:human· 1

Frequent Co-Authors

Tianzhou MaiD· 3 shared Jianfei QiiD· 2 shared Shuo CheniD· 2 shared Zhenyao YeiD· 2 shared Chen MoiD· 1 shared Eric Li· 1 shared Feng Li· 1 shared George C. TsengiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersBioinformatics

Tianzhou MaORCID

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3 papers

Recent·Most cited

Inferring non-coding RNA regulatory network from transcriptomic data and curated databases

Hongjie Ke, Zhenyao Ye, Feng Li, Zixin Xu, Rong Pan, Eric Li, Jianfei Qi, Shuo Chen, Menglu Liang, Tianzhou Ma

The paper introduces CAR-NET, a framework for inferring non-coding RNA regulatory networks using transcriptomic data and curated databases, showing its effectiveness in simulations and case studies.

bioRxiv · Apr 10, 2025 · PreprintBioinformatics

High-dimension to high-dimension screening for detecting genome-wide epigenetic regulators of gene expression

Hongjie Ke, Zhao Ren, Shuo Chen, George C. Tseng, Jianfei Qi, Tianzhou Ma

The paper introduces a screening method to identify genome-wide epigenetic regulators of gene expression by reducing dimensionality in both predictors and responses.

bioRxiv · Feb 21, 2022 · PreprintBioinformatics

A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures

Chen Mo, Zhenyao Ye, Hongjie Ke, Tong Lu, Travis A. Canida, Song Liu, Qiong Wu, Zhiwei Zhao, Yizhou Ma, Liyi Elliot Hong, Peter V Kochunov, Tianzhou Ma, Shuo Chen

This paper introduces a new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures and finds it outperforms existing methods in sensitivity, bias, and false discovery rate.

bioRxiv · Oct 1, 2021 · PreprintBioinformatics