Author

Leisha A. Emens

Kaiser PermanenteORCID 0000-0002-7694-0731Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Organism:human· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Aleksander S. PopeliD· 2 shared Cesar Augusto Santa-MariaiD· 2 shared Ravi VaradhaniD· 2 shared Ashley Cimino‐MathewsiD· 1 shared Atul M. DeshpandeiD· 1 shared Elana Judith FertigiD· 1 shared Elizabeth M. JaffeeiD· 1 shared Hanwen WangiD· 1 shared

Activity by Year

2023-2024 · peak 1/yr

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10 top collaborators · 35 mutual co-author links

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2 shared papersSystems Biology

Aleksander S. PopelORCID

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2 papers

Recent·Most cited

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Theinmozhi Arulraj, Hanwen Wang, Atul M. Deshpande, Ravi Varadhan, Leisha A. Emens, Elizabeth M. Jaffee, Elana Judith Fertig, Cesar Augusto Santa-Maria, Aleksander S. Popel

The paper uses virtual patient models to evaluate biomarkers for predicting response to PD-1 blockade in TNBC, finding that early on-treatment biomarkers and blood-based markers offer better performance.

Proceedings of the National Academy of Sciences · Oct 28, 2024Peer-reviewedSystems Biology

Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer

Haoyang Mi, Ravi Varadhan, Ashley Cimino‐Mathews, Leisha A. Emens, Cesar Augusto Santa-Maria, Aleksander S. Popel

The paper identifies spatial and compositional biomarkers in the tumor microenvironment of triple-negative breast cancer that predict clinical outcomes using imaging mass cytometry and deep learning.

bioRxiv · Dec 18, 2023 · PreprintBioinformatics