Author

Thomas H. Colligan

University of ArizonaORCID 0000-0003-4315-2074Unclaimed 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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Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Assay:protein sequence· 1

Frequent Co-Authors

Travis J. WheeleriD· 2 shared Daniel R. OlsoniD· 1 shared Daphne DemekasiD· 1 shared Douglas J. EmleniD· 1 shared Jack W. RoddyiD· 1 shared Kayla Irish· 1 shared Ken Youens‐ClarkiD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 13 mutual co-author links

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2 shared papersBioinformatics

Travis J. WheelerORCID

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2 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedJul 1, 2025

NEAR: Neural Embeddings for Amino acid Relationships

Daniel R. Olson, Thomas H. Colligan, Daphne Demekas…·Travis J. Wheeler

NEAR improves homology detection speed and accuracy by using neural embeddings trained with contrastive learning, outperforming existing methods as a pre-filter for protein sequence annotation.

2 citations

protein sequencecontrastive learning

𝕏 PostBluesky

BioinformaticsPLoS ONEPeer-reviewedJul 26, 2023

DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals

Thomas H. Colligan, Kayla Irish, Douglas J. Emlen, Travis J. Wheeler

DISCO is a deep learning tool for segmenting acoustic signals with confidence estimates, demonstrated on beetle courtships and whale songs.

4 citations

𝕏 PostBluesky