Author

Zuqi Li

University of LiègeORCID 0000-0002-2318-9753Unclaimed 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:clustering· 3 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:batch correction / integration· 1 Research Focus:benchmarking· 1 Organism:human· 1 Assay:imaging· 1 Approach:matrix factorization· 1

Frequent Co-Authors

Kristel Van SteeniD· 3 shared Peter D. ClaesiD· 3 shared David W. FardoiD· 2 shared Mark D. ShriveriD· 2 shared Mary L. MarazitaiD· 2 shared Seth Michael WeinbergiD· 2 shared Susan WalshiD· 2 shared Bertram Müller‐MyhsokiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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3 shared papersBioinformatics

Kristel Van SteenORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

MetaClaw: an auditable AI agent for end-to-end, multi-directional metagenomic and multi-omics analysis

Haohong Zhang, Zuqi Li, Philip Naderev Panuringan Lagniton, Zi Wang, Linfeng Zhao, W.Q. Li, pengmin Duan, Xiaosen Jiang, Kang Ning

MetaClaw is an auditable AI agent that performs end-to-end metagenomic and multi-omics analysis, recovering key biological markers across multiple studies.

bioRxiv · Sep 3, 2026 · PreprintBioinformatics

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data

Zuqi Li, Sam F. L. Windels, Noël Malod‐Dognin, Seth Michael Weinberg, Mary L. Marazita, Susan Walsh, Mark D. Shriver, David W. Fardo, Peter D. Claes, Nataša Pržulj, Kristel Van Steen

INMTD integrates omics and 3D imaging data to produce unconfounded individual subgroups with biologically meaningful embeddings.

Bioinformatics · Mar 20, 2025Peer-reviewedBioinformatics

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

Zuqi Li, Sonja Katz, Edoardo Saccenti, David W. Fardo, Peter D. Claes, Vítor A. P. Martins dos Santos, Kristel Van Steen, Gennady V. Roshchupkin

The paper introduces four VAE-based deconfounding frameworks for multi-omics clustering, showing they reduce confounding effects and improve biological clustering.

Briefings in Bioinformatics · Sep 23, 2024Peer-reviewedGenomics

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Zuqi Li, Federico Melograna, Hanne Hoskens, Diane Duroux, Mary L. Marazita, Susan Walsh, Seth Michael Weinberg, Mark D. Shriver, Bertram Müller‐Myhsok, Peter D. Claes, Kristel Van Steen

netMUG identifies actionable subgroups using network-guided multi-view clustering, showing improved performance over existing methods in synthetic and real data.

Frontiers in Genetics · Dec 6, 2023Peer-reviewedBioinformatics