Author

TingFang Lee

New York UniversityORCID 0000-0003-4939-7136Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:statistical model· 3 Assay:metagenomics· 2 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Organism:human· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Jiyuan HuiD· 3 shared Ann Marie SchmidtiD· 1 shared Boyan ZhouiD· 1 shared Chan WangiD· 1 shared Gadi WollsteiniD· 1 shared Hongping GuoiD· 1 shared Huilin LiiD· 1 shared Joel S. SchumaniD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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3 shared papersBioinformatics

Jiyuan HuORCID

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3 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedJan 21, 2026

Joint Modeling of Longitudinal Biomarker and Survival Outcomes with the Presence of Competing Risk in Nested Case-Control Studies with Application to the TEDDY Microbiome Dataset

Yanan Zhao, TingFang Lee, Boyan Zhou…·Jiyuan Hu

The paper introduces JM-NCC, a joint modeling framework for nested case-control studies with competing risks, applied to the TEDDY microbiome dataset to analyze biomarker and survival outcomes.

DOI 10.1093/bioinformatics/btag038

metagenomicsstatistical model

𝕏 PostBluesky

BioinformaticsTranslational Vision Science & TechnologyPeer-reviewedJan 19, 2024

LIMBARE: an Advanced Linear Mixed-effects Breakpoint Analysis with Robust Estimation Method with Applications to Longitudinal Ophthalmic Studies

TingFang Lee, Joel S. Schuman, Maria de los Angeles Ramos Cadena…·Jiyuan Hu

LIMBARE improves breakpoint estimation in longitudinal ophthalmic studies by using robust linear mixed-effects modeling, outperforming other methods in simulations and real data.

2 citations

humanstatistical model

𝕏 PostBluesky

BioinformaticsFrontiers in Cellular and Infection MicrobiologyPeer-reviewedOct 28, 2022

A maximum-type microbial differential abundance test with application to high-dimensional microbiome data analyses

Zhengbang Li, Xiaochen Yu, Hongping Guo…·Jiyuan Hu

The paper introduces a new statistical test for detecting differential abundance in high-dimensional microbiome data, showing improved power and control of type I error compared to existing methods.

3 citations

metagenomicsstatistical model

𝕏 PostBluesky