Author

Akshat Singh

University of California Davis Medical Center

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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Genomics· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:imaging· 1 Approach:optimal transport· 1

Frequent Co-Authors

Aditya SivakumariD· 1 shared Angela R. SaboiD· 1 shared Anish Tatke· 1 shared Daria BarwinskaiD· 1 shared Kaifeng CheniD· 1 shared Kuang‐Yu JeniD· 1 shared Malhar Bhide· 1 shared Michael J. FerkowicziD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperGenomics

Aditya SivakumarORCID

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2 papers

Recent·Most cited

Muon Reduces the Training Cost of Regulatory DNA Transformers

Viraj Doshi, Malhar Bhide, Akshat Singh, Yash Rathod, Aditya Sivakumar

This paper compares Adam and Muon optimizers for training regulatory DNA Transformer models, finding Muon reduces compute costs while achieving similar performance.

bioRxiv · Jul 22, 2026 · PreprintGenomics

DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images

Nicholas Lucarelli, Seth Winfree, Angela R. Sabo, Daria Barwinska, Michael J. Ferkowicz, William S. Bowen, Akshat Singh, Kaifeng Chen, Anish Tatke, Kuang‐Yu Jen, Michael T. Eadon, Tarek M. El‐Achkar, Sanjay Jain, Pinaki Sarder

DigitAb predicts cell types from H&E-stained slides using deep learning, achieving high accuracy without specialized assays or immunostains.

bioRxiv · May 21, 2026 · PreprintPathology