Author

G. Srinivasan

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

Biology Disciplines

Click to filter author papers

Pathology· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 3 Assay:imaging· 3 Research Focus:cell type annotation· 2 Research Focus:clustering· 2 Approach:deep learning· 2 Assay:spatial transcriptomics· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

B. Christensen· 3 shared J. Levy· 3 shared L. Vaickus· 3 shared Y. Lu· 3 shared A. Diallo· 2 shared F. Kolling· 2 shared L. Salas· 2 shared M. Davis· 2 shared

Activity by Year

2023-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersPathology

B. Christensen

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

DeltaAI: Semi-Autonomous Tissue Grossing Measurements and Recommendations using Neural Radiance Fields for Rapid, Complete Intraoperative Histological Assessment of Tumor Margins

A. Suvarna, R. Vempati, R. Chacko, G. Srinivasan, Y. Lu, B. Hunt, V. Torres, K. Samkoe, M. Davis, L. Fu, B. Christensen, L. Vaickus, M. LeBoeuf, J. Levy

DeltaAI uses Neural Radiance Fields to create 3D tissue models for faster and more accurate intraoperative tumor margin assessment in Mohs Micrographic Surgery.

bioRxiv · Aug 7, 2023 · PreprintPathology

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining

G. Srinivasan, M. Davis, M. LeBoeuf, M. Fatemi, Z. Azher, Y. Lu, A. Diallo, M. Montivero, F. Kolling, L. Perrard, L. Salas, B. Christensen, S. Palisoul, G. Tsongalis, L. Vaickus, , J. Levy

This paper develops machine learning models to infer spatial transcriptomics data from H&E-stained skin slides, improving the assessment of photoaging and its therapeutic implications.

bioRxiv · Jul 30, 2023 · PreprintPathology

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

Z. Azher, M. Fatemi, Y. Lu, G. Srinivasan, A. Diallo, B. Christensen, L. Salas, F. Kolling, L. Perreard, S. Palisoul, L. Vaickus, J. Levy

This paper applies spatial transcriptomics data with contrastive crossmodal pretraining to improve graph-based deep learning models for cancer histopathology analysis.

bioRxiv · Jul 30, 2023 · PreprintPathology