Author

A. Jacobsen Skanderup

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 4

Signature Assays, Organisms & Methods

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Organism:human· 4 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Research Focus:cell-cell communication· 1

Frequent Co-Authors

E. Revkov· 3 shared M. M. Chang· 2 shared Y. A. Guo· 2 shared A. James· 1 shared A. L. K. Tan· 1 shared A. N. Kaya· 1 shared A. Takano· 1 shared A. Tan· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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3 shared papersCancer Biology

E. Revkov

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Author profile →

4 papers

Recent·Most cited

A transcriptome-wide meta-analysis reveals lack of cancer-cell intrinsic determinants of response to immune checkpoint blockade

Y. A. Guo, T. Kulshrestha, M. M. Chang, I. Kassam, E. Revkov, S. Rizzetto, A. Tan, D. Tan, I. B. Tan, A. Jacobsen Skanderup

This study performs a transcriptome-wide meta-analysis to identify determinants of immune checkpoint blockade response, finding that stromal factors, not cancer-cell intrinsic features, are key predictors across tumor types.

bioRxiv · Jul 30, 2023 · PreprintCancer Biology

Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study

M. U. Oner, J. Chen, E. Revkov, A. James, S. Y. Heng, A. N. Kaya, J. J. S. Alvarez, A. Takano, X. M. Cheng, T. K. H. Lim, D. S. W. Tan, W. Zhai, A. Jacobsen Skanderup, W.-K. Sung, H. K. Lee

This paper develops a deep multiple instance learning model to predict tumor purity from histopathology slides, showing spatial variation and improving sample selection for genomic analysis.

bioRxiv · Jul 8, 2021Cancer Biology

Integrative Epigenomic and High-Throughput Functional Enhancer Profiling Reveals Determinants of Enhancer Heterogeneity in Gastric Cancer

T. Sheng, S. W. T. Ho, W. F. Ooi, C. Xu, M. Xing, N. Padmanabhan, K. K. Huang, L. Ma, M. Ray, Y. A. Guo, S. N. Leng, C. G. Anene-Nzelu, M. M. Chang, M. Razavi-Mohseni, M. Beer, , P. Tan

The paper identifies enhancer heterogeneity in gastric cancer by integrating epigenomic data and functional assays, linking it to genetic and regulatory factors.

bioRxiv · Jun 9, 2021Cancer Biology

Data-driven inference of crosstalk in the tumor microenvironment

U. Ghoshdastider, M. M. Naeini, N. Rohatgi, E. Revkov, A. Wong, S. Solai, T. T. Nguyen, J. Yeong, J. Iqbal, P. H. Tan, B. Chowbay, R. DasGupta, A. Jacobsen Skanderup

The paper develops a data-driven method to deconvolute bulk tumor transcriptomes and predict cancer-stromal crosstalk in 20 solid tumor types, revealing new insights into signaling patterns.

bioRxiv · Jun 15, 2020 · PreprintCancer Biology