Author

James Paul Agolia

Stanford MedicineORCID 0000-0001-5357-3871Unclaimed ORCID profile · follow to get notified of new preprints

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Research Focus:drug response / drug discovery· 3 Organism:human· 3 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Assay:scRNA-seq· 2 Approach:statistical model· 2 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Amanda KiraneiD· 3 shared Daniel DelittoiD· 3 shared Aaron M. NewmaniD· 2 shared Amanda GonçalvesiD· 2 shared Andrea E. Delitto· 2 shared Angela D Tabora· 2 shared Biren ReddyiD· 2 shared Byrne LeeiD· 2 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersCancer Biology

Amanda KiraneORCID

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3 papers

Recent·Most cited

A Pan-Cancer Single-Cell Atlas to Evaluate Tumor Identity, Cell Line Concordance, and Dependency Mapping

Rosyli F. Reveron‐Thornton, James Paul Agolia, Chuner Guo, Maria Moozhiyil Korah, Chia-Hsin Hsu, Peter Y. Xie, Renceh A. B. Flojo, Andrea E. Delitto, Amanda Gonçalves, Angela D Tabora, Michael Januszyk, Victoria E Sanchez, Kevin Nee, Biren Reddy, Wesley Bobst, , Daniel Delitto

The paper constructs a pan-cancer single-cell atlas to improve tumor identity inference and identify genetic vulnerabilities by integrating high-quality malignant cells across multiple cancers.

bioRxiv · Feb 24, 2026 · PreprintCancer Biology

Multidimensional Single-Cell Transcriptomic Profiling of Uterine Leiomyosarcomas Reveals Distinct Tumor States with Inferred Therapeutic Vulnerabilities

Maria Moozhiyil Korah, James Paul Agolia, Biren Reddy, Renceh A. B. Flojo, Amanda Costa Gonçalves, Lilin Wang, Rishik Bethi, Wesley Bobst, Brooke Liang, Kaylin A. Yip, Angela D Tabora, Chia-Hsin Hsu, Rosyli F. Reveron‐Thornton, Chuner Guo, Robert F. Gruener, , Daniel Delitto

This study uses single-cell and spatial transcriptomics to identify distinct tumor states in uterine leiomyosarcomas and proposes drug targets based on these states.

bioRxiv · Nov 17, 2025 · PreprintCancer Biology

Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman Spectroscopy and AI

Kai Chang, Mamatha Serasanambati, Baba Ogunlade, Hsiu-Ju Hsu, James Paul Agolia, Ariel Stiber, Jeffrey Gu, Jay Chadokiya, Grayson E. Rodriguez, Prabhjeet Singh, Saurabh Sharma, Amanda Gonçalves, Ojasvi Verma, Fareeha Safir, Nhat Vu, , Jennifer A. Dionne

This paper uses single-cell Raman spectroscopy and machine learning to predict therapeutic response in melanoma, achieving >96% accuracy in cell line classification and 91% accuracy in patient drug resistance inference.

bioRxiv · May 16, 2025 · PreprintCancer Biology