Scholar
Marianna Rapsomaniki
Google Scholar ID: fFiBRAIAAAAJ
University of Lausanne (UNIL), Lausanne University Hospital (CHUV)
Citations & Impact
Citations
1,428
H-index
13
i10-index
15
Publications
20
Co-authors
10
list available
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- 1. Ovchinnikova, K., Born, J., Chouvardas, P., Rapsomaniki, M. * and Kruithof de Julio, M.* (2024). 'Overcoming limitations in current measures of drug response may enable AI driven precision oncology.' npj Precision Oncology, 8, 95.
- 2. Gossi, F., Pati P., Chouvardas, P., Martinelli, A. L., Kruithof-de Julio, M., Rapsomaniki, M. A (2023). 'Matching single cells across modalities with contrastive learning and optimal transport.' Brief Bioinform 24(3).
- 3. Martinelli, A. L., Rapsomaniki, M. A (2022). 'ATHENA: analysis of tumor heterogeneity from spatial omics measurements.' Bioinformatics 38(11): 3151-3153.
- 4. Rapsomaniki, M. A., Maxouri, S., Nathanailidou, P., Garrastacho, M. R., Giakoumakis, N. N., Taraviras, S., Lygeros, J., Lygerou, Z. (2021). 'In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity.' NAR Genom Bioinform 3(1): lqaa112.
- 5. Wagner, J., Rapsomaniki, M. A., Chevrier, S., Anzeneder, T., Langwieder, C., Dykgers, A., Rees, M., Ramaswamy, A., Muenst, S., Soysal, S. D., Jacobs, A., Windhager, J., Silina, K., van den Broek, M., Dedes, K. J., Rodriguez Martinez, M., Weber, W. P., Bodenmiller, B. (2019). 'A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer.' Cell 177(5): 1330-1345 e1318.
- 6. Rapsomaniki, M. A., Lun, X. K., Woerner, S., Laumanns, M., Bodenmiller, B., Martinez, M. R. (2018). 'CellCycleTRACER accounts for cell cycle and volume in mass cytometry data.' Nat Commun 9(1): 632.
Research Experience
- Tenure Track Assistant Professor, Research Lead, AI/ML for Biomedicine group leader
Background
- Research interests include developing AI/ML approaches tailored to complex and multi-modal biomedical data to elucidate cancer mechanisms, from the intra-cellular to the patient level. Topics of special interest include understanding 3D genome regulation, modeling the tumor microenvironment using single-cell omics (transcriptomics, metabolomics, proteomics), and developing AI-driven precision oncology approaches based on predicting the effects of drug perturbations ex vivo.
