Author

Akshaya Thoutam

Broad Institute

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Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 3 Research Focus:benchmarking· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Ava P. AminiiD· 3 shared Lorin A. CrawfordiD· 3 shared Madeline HughesiD· 3 shared Peter S. WinteriD· 3 shared Srivatsan RaghavaniD· 3 shared Anay Gupta· 2 shared Andrew W. NaviaiD· 2 shared Nicolò FusiiD· 2 shared

Activity by Year

2025-2026 · peak 2/yr

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10 top collaborators · 44 mutual co-author links

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3 shared papersBioinformatics

Ava P. AminiORCID

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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

Adaptive resampling for improved machine learning in imbalanced single-cell datasets

Zeinab Navidi, Akshaya Thoutam, Madeline Hughes, Srivatsan Raghavan, Peter S. Winter, Lorin A. Crawford, Ava P. Amini

This paper introduces an adaptive resampling method to improve machine learning performance on imbalanced single-cell datasets, showing better results in gene expression reconstruction, cell type classification, and perturbation response prediction.

bioRxiv · Nov 4, 2025 · PreprintBioinformatics

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