Author

C. Clementi

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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Biophysics· 2 Biochemistry· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Approach:deep learning· 3 Organism:human· 3 Assay:proteomics· 3 Approach:experimental assay· 2 Assay:CITE-seq· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

B. Kuropka· 2 shared C. Freund· 2 shared A. Mardt· 1 shared B. Bruegger· 1 shared D. Haxhiraj· 1 shared D. Rosenberger· 1 shared D. Speck· 1 shared E. Brencher· 1 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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2 shared papersBiochemistry

B. Kuropka

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

T cell interaction partners of DHHC20

D. Haxhiraj, B. Kuropka, E. Brencher, D. Rosenberger, H. Brandt, D. Speck, P. Scheerer, B. Bruegger, C. Clementi, C. Freund

This study identifies DHHC20 interaction partners in T cells, showing it interacts with Gsβ1γ2 and suggests a model for its role in palmitoylation.

bioRxiv · Feb 14, 2024 · PreprintBiochemistry

Decoding of T cell immunodominance patterns by an integrated mechanistic approach

M. Alvaro-Benito, E. T. Abualrous, H. Lingel, S. Meltendorf, J. Holzapfel, P. de Diego Valera, J. Sticht, B. Kuropka, C. Clementi, F. Kuppler, M. C. Brunner-Weinzierl, C. Freund

The paper identifies immunodominant CD4+ T cell epitopes for SARS-CoV-2 antigens by integrating antigen processing systems with in silico tools, revealing distinct processing mechanisms for different antigens.

bioRxiv · Jan 10, 2024 · PreprintImmunology

Deep learning to decompose macromolecules into independent Markovian domains

A. Mardt, T. Hempel, C. Clementi, F. Noe

This paper introduces a method combining independent Markov decomposition with VAMPnets to decompose large molecular systems into Markovian subdomains, improving data efficiency and interpretability.

bioRxiv · Mar 30, 2022Biophysics

Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire

X. Lin, J. T. George, N. P. Schafer, K. Ng Chau, M. E. Birnbaum, C. Clementi, J. N. Onuchic, H. Levine

This paper introduces RACER, a pairwise energy model that assesses TCR-peptide affinity using supervised machine learning, demonstrating its utility in simulating thymic selection and estimating recognition rates for tumor antigens.

bioRxiv · Apr 6, 2020Biophysics