Author

Simrah Khan

Helmholtz Munich

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· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:imaging· 1 Assay:metabolomics· 1 Organism:mouse· 1

Frequent Co-Authors

Fabian Joachim TheisiD· 2 shared Alejandro Tejada-LapuertaiD· 1 shared Ariane Eceiza· 1 shared Carina Steinchen· 1 shared Christoph H. MayriD· 1 shared David LaueriD· 1 shared Eshita Jain· 1 shared Gerald BurgstalleriD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersCell Biology

Fabian Joachim TheisORCID

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2 papers

Recent·Most cited

Single cell decomposition of multicellular aging programs associated with impaired lung regeneration

Janine Gote-Schniering, M. Camila Melo-Narváez, Gowtham Boosarpu, David Lauer, Sai Rama Sridatta Prakki, Huijuan Wang, Matthias Brunner, Simrah Khan, Meshal Ansari, Michael Ammeter, Eshita Jain, Nikola Puda, Konstantin Wiedemann, Carina Steinchen, Sara Ashgapour, , Herbert B. Schiller

The paper uses single-cell RNA-seq to analyze aging effects on lung regeneration, identifying immune cell states linked to impaired progenitor function in aged mice.

bioRxiv · Jul 24, 2025 · PreprintCell Biology

Scalable and universal prediction of cellular phenotypes enables in silico experiments

Yuge Ji, Alejandro Tejada-Lapuerta, Niklas Arndt Schmacke, Zihe Zheng, Xinyue Zhang, Simrah Khan, Ina Rothenaigner, Juliane Tschuck, Kamyar Hadian, Veit Hornung, Fabian Joachim Theis

Prophet predicts cellular phenotypes from perturbations using transfer learning, outperforming baselines and enabling in silico screening for therapeutic discovery.

bioRxiv · Aug 12, 2024 · PreprintCell Biology