Author

Yun Chiang

University of CopenhagenORCID 0000-0003-3605-4054Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:proteomics· 2 Research Focus:software / tool· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

Matthew J. CollinsiD· 3 shared Bharath NairiD· 2 shared Benjamin DartiguesiD· 1 shared Ismael Rodríguez PalomoiD· 1 shared Jesper Velgaard OlseniD· 1 shared Joannes Adrianus Antonius DekkeriD· 1 shared Max RamsøeiD· 1 shared Meaghan MackieiD· 1 shared

Activity by Year

2024-2024 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersBioinformatics

Matthew J. CollinsORCID

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3 papers

Recent·Most cited

BioinformaticsPeer Community JournalPeer-reviewedNov 21, 2024

Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins

Ismael Rodríguez Palomo, Bharath Nair, Yun Chiang…·Matthew J. Collins

The paper benchmarks sequencing tools for identifying degraded proteins in ancient samples, finding optimal strategies for peptide and PTM detection.

11 citations

proteomicsstatistical model

𝕏 PostBluesky

BioinformaticsbioRxivPreprintNov 15, 2024

Orthrus: an AI-powered, cloud-ready, and open-source hybrid approach for metaproteomics

Yun Chiang, Matthew J. Collins

Orthrus improves metaproteomics analysis by combining de novo sequencing and database searching, outperforming existing tools in peptide output and taxonomic diversity.

5 citations

metagenomicsdeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintNov 15, 2024

Anubis: a multi-level authentication scale for ancient proteins using random forest classification

Yun Chiang, Bharath Nair, Max Ramsøe…·Matthew J. Collins

Anubis authenticates ancient proteins using deamidation patterns and random forest classification, demonstrating reliability with ancient and degraded beta-lactoglobulin.

3 citations

proteomicsmachine learning (classical)

𝕏 PostBluesky