Biology 2.0 research at the intersection of single-cell transcriptomics, artificial intelligence, and reproducible computational biology
Dynamic analysis of cellular states, trajectories, and heterogeneity
Multi-method validation, quantitative state indices, and reproducible workflows
Computational frameworks for cancer biology and treatment response
Awarded under the FENIX 2025 call of PRACE, this project develops a robust and reproducible framework for analysing dynamic cellular states in human single-cell transcriptomic data.
The workflow combines RNA velocity with quantitative cell-state indices and a multi-method robustness panel. Fifteen complementary inference methods will be compared across non-pathological reference atlases and cancer datasets, including therapy resistance.
The project is supported by the GAIA Cloud platform at CINECA, Italy, with 96 vCPUs, 1.5 TB of persistent storage, and one public IP address for 12 months. Project activation is scheduled for September 2026.
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