Author

Heidi Lyng

Oslo University HospitalORCID 0000-0002-7938-0514Unclaimed ORCID profile · follow to get notified of new preprints

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Heidi Lyng1 paper

Biology Disciplines

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Cell Biology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:imaging· 1 Approach:statistical model· 1

Frequent Co-Authors

Beáta GrallertiD· 1 shared Christina Sæten FjeldboiD· 1 shared Gozde N. GunesliiD· 1 shared Kari-Anne Frikstad Valen· 1 shared Laura Marian Valencia-Pesqueira· 1 shared Lawrence S. YoungiD· 1 shared Lilian Lindbergsengen· 1 shared Nasir RajpootiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

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10 top collaborators · 21 mutual co-author links

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1 shared paperCell Biology

Beáta GrallertORCID

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2 papers

Recent·Most cited

Cell BiologybioRxivPreprintMar 18, 2025

Analysis of patient data reveals novel cancer-relevant functions for GCN2/eIF2αK4

Vilte Stonyte, Christina Sæten Fjeldbo, Priyanka Swaminathan…·Beáta Grallert

The paper correlates GCN2 expression with genes involved in stress responses, mitosis, and cell migration in cervical cancer samples, revealing novel cancer-relevant functions.

DOI 10.1101/2025.03.18.643965

humanbulk RNA-seq

𝕏 PostBluesky

Pathologynpj Precision OncologyPeer-reviewedJan 11, 2025

A Deep Learning Framework for Predicting Prognostically Relevant Consensus Molecular Subtypes in HPV-Positive Cervical Squamous Cell Carcinoma from Routine Histology Images

Ruoyu Wang, Gozde N. Gunesli, Vilde Eide Skingen…·Nasir Rajpoot

This paper develops a deep learning framework to predict consensus molecular subtypes in HPV-positive cervical cancer from histology images, showing significant survival stratification and TME differences between subtypes.

11 citations

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