- Adriano Luca Martinelli
- Maria Anna Rapsomaniki
- 2022
- Bioinformatics
AI for Single-cell Research
Understanding spatiotemporal heterogeneity across different scales of biological organization.
Overview
Our team’s research goal is understanding spatiotemporal heterogeneity across different scales of biological organization, with a focus on how it affects cancer initiation and progression. To achieve this, we combine (spatial) single-cell measurements with artificial intelligence and machine learning approaches to model the complexity of the tumor microenvironment. Understanding heterogeneity across multiple scales has the potential to lead to exciting mechanistic discoveries and pave the way for new precision medicine approaches.
Projects related to this work include:
Publications
- Aditya Kashyap
- Maria Anna Rapsomaniki
- et al.
- 2022
- TIBTECH
- Tristan Meynier
- Marianna Rapsomaniki
- 2021
- NeurIPS 2021
- Anna Fomitcheva Khartchenko
- Maria Anna Rapsomaniki
- et al.
- 2021
- PLoS ONE
- Johanna Wagner
- Maria Anna Rapsomaniki
- et al.
- 2019
- Cell
- Maria Anna Rapsomaniki
- Xiao Kang Lun
- et al.
- 2018
- Nature Communications
- Adriano Luca Martinelli
- Johanna Wagner
- et al.
- 2022
- STAR Protocols
- Jonas Windhager
- Amelia Paine
- et al.
- 2022
- bioRxiv
Resources
Contributors
Related projects
AI Methods for Precision Therapies
Developing AI methods for heterogeneity-aware precision therapies.
Modeling Spatial Heterogeneity of the Tumor Microenvironment
Combining spatial single-cell omics with AI to model the complexity of the tumor micorenvironment and enable novel spatial biomarker discovery.
Modeling the 4D Genome
Modeling the 4D genome with deep learning and stochastic simulations.
Quantifying Biological Heterogeneity from Single-cell Data
Understanding, modeling and quantifying different sources of heterogeneity from single-cell measurements.