Author

Anay Gupta

Georgia Institute of Technology

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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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:scRNA-seq· 2 Approach:deep learning· 1 Assay:DNA methylation· 1 Organism:human· 1 Approach:statistical model· 1 Approach:transformer / foundation model· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Akshaya Thoutam· 2 shared Andrew W. NaviaiD· 2 shared Ava P. AminiiD· 2 shared Lorin A. CrawfordiD· 2 shared Madeline HughesiD· 2 shared Nicolò FusiiD· 2 shared Peter S. WinteriD· 2 shared Srivatsan RaghavaniD· 2 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 44 mutual co-author links

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2 shared papersBioinformatics

Akshaya Thoutam

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3 papers

Recent·Most cited

Evaluating the role of pre-training dataset size and diversity on single-cell foundation model performance

Alan DenAdel, Madeline Hughes, Akshaya Thoutam, Anay Gupta, Andrew W. Navia, Nicolò Fusi, Srivatsan Raghavan, Peter S. Winter, Ava P. Amini, Lorin A. Crawford

This study evaluates how pre-training dataset size and diversity affect single-cell foundation model performance on zero-shot and fine-tuned tasks using 22.2 million cells.

Nature Methods · Jun 9, 2026Peer-reviewedBioinformatics

Polygraph: A Software Framework for the Systematic Assessment of Synthetic Regulatory DNA Elements

Avantika Lal, Laura M. Gunsalus, Anay Gupta, Tommaso Biancalani, Gökçen Eraslan

Polygraph assesses synthetic regulatory DNA elements using features like diversity and motif composition to improve gene therapy design.

Genome biology · May 6, 2025Peer-reviewedBioinformatics

Consequences of training data composition for deep learning models in single-cell biology

Ajay Nadig, Akshaya Thoutam, Madeline Hughes, Anay Gupta, Andrew W. Navia, Nicolò Fusi, Srivatsan Raghavan, Peter S. Winter, Ava P. Amini, Lorin A. Crawford

This study examines how training data composition affects deep learning models in single-cell transcriptomics, finding poor generalization to new cell types and benefits from including embryonic stem cell differentiation data.

bioRxiv · Feb 19, 2025 · PreprintGenomics