Author

Yue Fang

Oregon Health & Science UniversityORCID 0000-0002-6907-7191Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Yue Fang1 paper · Indiana University Indianapolis

Biology Disciplines

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Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Assay:metabolomics· 1

Frequent Co-Authors

Anjun MaiD· 2 shared Chi ZhangiD· 2 shared Haiqi ZhuiD· 2 shared Jia Wang· 2 shared Pengtao DangiD· 2 shared Qin Ma· 2 shared Sha CaoiD· 2 shared Changlin WaniD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersSystems Biology

Anjun MaORCID

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2 papers

Recent·Most cited

A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data

Haiqi Zhu, Changlin Wan, Min Yang, Yue Fang, Zheng An, Paveethran Swaminathan, Pengtao Dang, Zhi Li, Jia Wang, Yijie Wang, Yabing Chen, Anjun Ma, Qin Ma, Mark R. Kelley, Sha Cao, , Chi Zhang

This paper introduces mmFEA, a kinetic-aware framework for estimating metabolic flux changes from transcriptomics and metabolomics data, showing improved accuracy over baseline methods.

bioRxiv · Oct 2, 2026 · PreprintSystems Biology

Transcriptomics-based modeling of methionine metabolism effectively estimates sample-wise DNA methylation activity and epigenetic aging

Tingbo Guo, Pengtao Dang, Yue Fang, Haiqi Zhu, Xiao Wang, Jia Wang, Anjun Ma, Qin Ma, Sha Cao, Chi Zhang

This paper develops TDMA, a transcriptomics-based model to estimate DNA methylation activity and epigenetic aging from bulk, single-cell, and spatial transcriptomics data.

bioRxiv · Feb 2, 2026 · PreprintBioinformatics