Author

R. Aneja

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· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Organism:human· 2 Research Focus:differential expression· 1 Approach:experimental assay· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

A. Balasubramanian· 1 shared A. Omilian· 1 shared A. Rao· 1 shared A. Sreekumar· 1 shared C. Ambrosone· 1 shared C. Gutierrez· 1 shared C. Morrison· 1 shared D. Jebakumar· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperCancer Biology

A. Balasubramanian

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2 papers

Recent·Most cited

Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in Black American and White American patients with TNBC

Q. Zhu, A. Balasubramanian, J. R. Asirvatham, M. Chatterjee, D. W. B. Piyarathna, J. Kaur, N. Mohamed, L. Wu, S. Wang, N. Pourfarrokh, P. D. Binsol, M. Bhargava, U. Rasaily, Y. Xu, J. Zheng, , A. Sreekumar

This study uses spatial omics to identify distinct tumor microenvironment profiles in Black and White American TNBC patients, linking them to different immunosuppressive mechanisms and clinical outcomes.

bioRxiv · Mar 17, 2024Cancer Biology

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

S. Bhattarai, G. Saini, H. Li, H. Duanmu, G. Seth, T. B. Fisher, E. A. M. Janssen, U. Kiraz, J. Kong, R. Aneja

This study uses machine learning to predict neoadjuvant chemotherapy response in triple-negative breast cancer by combining histological and molecular biomarkers.

bioRxiv · Apr 17, 2023 · PreprintCancer Biology