Author

S. Graf

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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Microbiology· 2 Cell Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell signaling & trafficking· 1 Research Focus:clustering· 1 Research Focus:development & morphogenesis· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. Cuenod· 2 shared A. Egli· 2 shared O. Dubuis· 2 shared A. Hata· 1 shared A. Prabhakar· 1 shared A. Widmer· 1 shared B. B. Graham· 1 shared B. N. Kharbikar· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersMicrobiology

A. Cuenod

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Reversal of pulmonary veno-occlusive disease phenotypes by inhibition of the integrated stress response

A. Prabhakar, R. Kumar, M. Wadhwa, P. Ghatpande, J. Zhang, Z. Zhao, C. O. Lizama, B. N. Kharbikar, S. Graf, C. M. Treacy, N. W. Morrell, B. B. Graham, G. Lagna, A. Hata

This study shows that inhibiting the integrated stress response (ISR) or PKR reduces vascular remodeling in PVOD models, suggesting potential therapeutic strategies.

bioRxiv · Nov 27, 2023 · PreprintCell Biology

Ancient methicillin-resistant Staphylococcus aureus: expanding current knowledge using molecular epidemiological characterization of a Swiss legacy collection

V. Benvenga, A. Cuenod, S. Purushothaman, G. Dasen, M. Weisser, S. Bassetti, T. Roloff, M. Siegemund, U. Heininger, J. A. Bielicki, M. Wehrli, P. Friderich, R. Frei, A. Widmer, K. Herzog, , A. Egli

The paper characterizes ancient MRSA isolates from 1965-1987 and compares them to modern isolates, identifying novel sequence types and lineage dynamics.

bioRxiv · Apr 27, 2023 · PreprintMicrobiology

Direct Antimicrobial Resistance Prediction from clinical MALDI-TOF mass spectra using Machine Learning

C. Weis, A. Cuenod, B. Rieck, F. Llinares-Lopez, O. Dubuis, S. Graf, C. Lang, M. Oberle, M. Brackmann, K. K. Soegaard, M. Osthoff, K. Borgwardt, A. Egli

This study uses machine learning to predict antimicrobial resistance from MALDI-TOF mass spectra, achieving high AUROC values and showing potential to improve clinical treatment decisions.

bioRxiv · Jul 30, 2020Microbiology