Author

Jiening Zhu

Stony Brook UniversityORCID 0000-0002-9506-073XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:clustering· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:optimal transport· 1 Assay:proteomics· 1

Frequent Co-Authors

Joseph O. DeasyiD· 4 shared Allen TannenbaumiD· 3 shared Corey WeistuchiD· 2 shared Jung Hun OhiD· 2 shared Anh Phong TraniD· 1 shared Anish K. SimhaliD· 1 shared Daniel GrangeiD· 1 shared Kevin A. MurgasiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 26 mutual co-author links

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4 shared papersBioinformatics

Joseph O. DeasyORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

ORCO: Ollivier-Ricci Curvature-Omics: an unsupervised method for analyzing robustness in biological systems

Anish K. Simhal, Corey Weistuch, Kevin A. Murgas, Daniel Grange, Jiening Zhu, Jung Hun Oh, Rena Elkin, Joseph O. Deasy

ORCO is an unsupervised network-based tool that computes Ollivier-Ricci curvature to analyze biological network robustness and identify therapeutic targets.

Bioinformatics · Feb 25, 2025Peer-reviewedBioinformatics

Multi-omic integrated curvature study on pan-cancer genomic data

Jiening Zhu, Anh Phong Tran, Joseph O. Deasy, Allen Tannenbaum

This paper introduces a network curvature framework to identify cancer subtypes from multi-omic data, showing significant survival differences across cancers.

Mathematics of Control Signals and Systems · Jul 12, 2023Peer-reviewedBioinformatics

The Maximum Entropy Principle For Compositional Data

Corey Weistuch, Jiening Zhu, Joseph O. Deasy, Allen Tannenbaum

This paper introduces a method based on the maximum entropy principle to model compositional data, demonstrating its effectiveness in capturing inter-species relationships and gene-gene interactions.

BMC Bioinformatics · Oct 29, 2022Peer-reviewedBioinformatics

vWCluster: A Network Based Clustering of Multi-omics Breast Cancer Data Based on Vector-Valued Optimal Transport

Jiening Zhu, Jung Hun Oh, Joseph O. Deasy, Allen Tannenbaum

The paper introduces vWCluster, a network-based clustering method for multi-omics breast cancer data using vector-valued optimal transport, identifying tumor subtypes with distinct survival rates and immune microenvironment profiles.

bioRxiv · Jun 17, 2021 · PreprintBioinformatics