Author

Hayden Stites

University of California San DiegoORCID 0000-0002-3448-2613Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Assay:imaging· 1 Organism:plant (other)· 1 Research Focus:software / tool· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Adam KlieiD· 1 shared Emma K. FarleyiD· 1 shared Hannah K. CarteriD· 1 shared Joe J. Solvason· 1 shared Tobias JoresiD· 1 shared Uri ManoriD· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 10 mutual co-author links

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1 shared paperBioinformatics

Adam KlieORCID

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2 papers

Recent·Most cited

PSSR2: a user-friendly Python package for democratizing deep learning-based point-scanning super-resolution microscopy

Hayden Stites, Uri Manor

PSSR2 provides a user-friendly Python package for applying deep learning-based super-resolution and denoising to undersampled microscopy data without requiring programming expertise.

BMC Methods · Jan 2, 2025Peer-reviewedBioinformatics

EUGENe: A Python toolkit for predictive analyses of regulatory sequences

Adam Klie, Hayden Stites, Tobias Jores, Joe J. Solvason, Emma K. Farley, Hannah K. Carter

EUGENe is a FAIR Python toolkit for deep learning analysis of regulatory sequences, offering modular tools for data handling, model training, and evaluation.

bioRxiv · Oct 24, 2022 · PreprintBioinformatics