Author

T. D. Kim

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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Cell Biology· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:development & morphogenesis· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Assay:electronic health records· 1 Organism:human· 1

Frequent Co-Authors

A. L. Schacht· 1 shared A. Moradian· 1 shared B. Lomenick· 1 shared C. D. Brody· 1 shared E. H. Jensen· 1 shared J. W. Pillow· 1 shared J. W. Thompson· 1 shared K. Krishnamurthy· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperCell Biology

A. L. Schacht

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2 papers

Recent·Most cited

Flow-field inference from neural data using deep recurrent networks

T. D. Kim, T. Z. Luo, T. Can, K. Krishnamurthy, J. W. Pillow, C. D. Brody

This paper introduces FINDR, a deep learning method that infers low-dimensional nonlinear dynamics from neural data, outperforming existing methods in capturing neuron responses and visualizing flow fields.

bioRxiv · Nov 14, 2023 · PreprintNeuroscience

Functional glycoproteomics by integrated network assembly and partitioning

M. E. Griffin, J. W. Thompson, Y. Xiao, M. J. Sweredoski, E. H. Jensen, Y. Koldobskaya, A. L. Schacht, T. D. Kim, P. Choudhry, B. Lomenick, S. D. Garbis, A. Moradian, L. C. Hsieh-Wilson

This paper introduces a systems-level approach to study O-GlcNAcylation by integrating AP-MS and chemoproteomics with network analysis, revealing both conserved and tissue-specific functions.

bioRxiv · Jun 13, 2023 · PreprintCell Biology