Author

Qiulin Huang

University of CambridgeORCID 0000-0002-3232-8436Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:gene regulatory network inference· 2 Research Focus:cell type annotation· 1 Assay:ChIP-seq· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Afshan McCarthyiD· 2 shared Claire S. SimoniD· 2 shared Kathy K. NiakaniD· 2 shared Kay T. ElderiD· 2 shared Leila Christie· 2 shared Phil Snell· 2 shared Thomas E. BartlettiD· 2 shared Alex Faulkner· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersDevelopmental Biology

Afshan McCarthyORCID

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3 papers

Recent·Most cited

Inferring gene-regulatory networks using epigenomic priors

Thomas E. Bartlett, Melodie Li, Chenyu Song, Yuche Gao, Qiulin Huang

This paper improves gene-regulatory network inference by using epigenomic priors, showing better accuracy with DNA methylation data compared to chromatin accessibility data.

iScience · Feb 28, 2026Peer-reviewedGenomics

Suppression of ERK signalling promotes pluripotent epiblast in the human blastocyst

Claire S. Simon, Afshan McCarthy, Laura M. Woods, Desislava P. Staneva, Martin Proks, Nazmus Salehin, Georgia Lea, Qiulin Huang, Madeleine Linneberg-Agerholm, Alex Faulkner, Athanasios Papathanasiou, Kay T. Elder, Phil Snell, Leila Christie, Patricia Garcia, , Kathy K. Niakan

The paper shows that inhibiting ERK signaling expands epiblast cells capable of becoming naive pluripotent stem cells in humans, while FGF stimulation expands hypoblast markers.

Nature Communications · Jul 28, 2025Peer-reviewedDevelopmental Biology

MICA: A multi-omics method to predict gene regulatory networks in early human embryos

Gregorio Alanis‐Lobato, Thomas E. Bartlett, Qiulin Huang, Claire S. Simon, Afshan McCarthy, Kay T. Elder, Phil Snell, Leila Christie, Kathy K. Niakan

The paper evaluates gene regulatory network prediction methods in early human embryos, finding that MICA, combining mutual information with chromatin accessibility, provides more reproducible and biologically meaningful results.

Life Science Alliance · Oct 25, 2023Peer-reviewedDevelopmental Biology