Author

T.-H. Kuo

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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Biochemistry· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:bacteria (other)· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:metabolomics· 1

Frequent Co-Authors

A. Bremges· 1 shared A. C. McHardy· 1 shared A. Khaledi· 1 shared A. Kola· 1 shared A. Oliver· 1 shared A. Weimann· 1 shared C.-C. Hsu· 1 shared D. Jonas· 1 shared

Activity by Year

2019-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperMicrobiology

A. Bremges

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Deep Lipidomics and Molecular Imaging of Unsaturated Lipid Isomers: A Universal Strategy Initiated by mCPBA Epoxidation

T.-H. Kuo, H.-H. Chung, H.-Y. Chang, M.-Y. Wang, T.-L. Shen, C.-C. Hsu

This paper introduces MELDI, a method to identify and map lipid isomers using epoxidation and mass spectrometry, demonstrating its application in human serum and adipocytes.

bioRxiv · Jun 2, 2019Biochemistry

Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

A. Khaledi, A. Weimann, M. Schniederjans, E. Asgari, T.-H. Kuo, A. Oliver, G. Cabot, A. Kola, P. Gastmeier, M. Hogardt, D. Jonas, M. Mofrad, A. Bremges, A. C. McHardy, S. Haeussler

This study uses machine learning on genomic and transcriptomic data to predict antimicrobial resistance in Pseudomonas aeruginosa, achieving high sensitivity for four antibiotics.

bioRxiv · May 24, 2019Microbiology