Author

Hellmut Merkle

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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Cancer Biology· 2

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:imaging· 1 Research Focus:metabolism & bioenergetics· 1 Assay:metabolomics· 1 Organism:mouse· 1

Frequent Co-Authors

Albert P. Chen· 2 shared Galen Reed· 2 shared James Mitchell· 2 shared Jan Henrik Ardenkjaer-Larsen· 2 shared Jeeva MunasingheiD· 2 shared Jeffrey R. BrenderiD· 2 shared Kazu Yamamoto· 2 shared Keita Saito· 2 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersCancer Biology

Albert P. Chen

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2 papers

Recent·Most cited

Cancer BiologybioRxivPreprintJan 4, 2019

Distinguishing Closely Related Pancreatic Cancer Subtypes In Vivo by 13C Glucose MRI without Hyperpolarization

Shun Kishimoto, Jeffrey R. Brender, Shingo Matsumoto…·Murali C. Krishna

The paper distinguishes closely related pancreatic cancer subtypes in vivo using 13C glucose MRI without hyperpolarization by detecting metabolic differences through tensor decomposition.

DOI 10.1101/511543

mouseimaging

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Cancer BiologybioRxivPreprintFeb 15, 2018

PET by MRI: Glucose Imaging by 13C-MRS without Dynamic Nuclear Polarization by Noise Suppression through Tensor Decomposition Rank Reduction

Jeffrey R. Brender, Shun Kishimoto, Hellmut Merkle…·Murali C. Krishna

This paper introduces a method to image glucose metabolism using 13C-MRS with tensor decomposition to enhance signal-to-noise ratio, enabling non-hyperpolarized imaging of the Warburg effect with high temporal and spatial resolution.

DOI 10.1101/265793

metabolomicsstatistical model

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