Author

E. Feng

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· 1 Immunology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:benchmarking· 1 Approach:biophysical modeling· 1 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

A. A. Ashkar· 1 shared A. Subramanian· 1 shared A. Zhang· 1 shared B. Christensen· 1 shared C. M. Wardell· 1 shared C. Sharma· 1 shared C. Y. Kang· 1 shared C.-K. C. Ding· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperImmunology

A. A. Ashkar

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A prostate cancer gastrointestinal transcriptional phenotype may be associated with diminished response to AR-targeted therapy

A. Subramanian, M. Zhang, M. Sharifi, T. Moreno-Rodriguez, E. Feng, N. R. Rydzewski, R. Shrestha, X. Zhu, S. G. Zhao, R. Aggarwal, E. J. Small, C.-K. C. Ding, D. A. Quigley, M. Sjöström

This study identifies a gastrointestinal gene expression phenotype in metastatic castration-resistant prostate cancer that correlates with reduced response to androgen receptor inhibitors.

bioRxiv · Jun 2, 2024 · PreprintCancer Biology

Targeting the MR1-MAIT Cell Axis Improves Vaccine Efficacy and Affords Protection against Viral Pathogens

R. Rashu, M. Ninkov, C. M. Wardell, J. M. Benoit, N. I. Wang, M. R. D'Agostino, A. Zhang, E. Feng, N. Saeedian, G. I. Bell, F. Vahedi, D. A. Hess, R. M. Troyer, C. Y. Kang, A. A. Ashkar, , S. M. M. Haeryfar

This study shows that targeting the MR1-MAIT cell axis with 5-OP-RU enhances vaccine efficacy against viral pathogens by expanding and reprogramming MAIT cells.

bioRxiv · Feb 20, 2023Immunology

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

M. Fatemi, E. Feng, C. Sharma, Z. Azher, T. Goel, O. Ramwala, S. Palisoul, R. Barney, L. Perreard, F. Kolling, L. A. Salas, B. Christensen, G. Tsongalis, L. Vaickus, J. Levy

This study investigates using deep learning to predict spatial transcriptomics data from whole slide images to assess colorectal tumor metastasis, finding that convolutional networks outperform transformer and graph-based models.

bioRxiv · Nov 24, 2022 · PreprintPathology