Sara Sommariva

This study benchmarks clustering methods for high-dimensional spike time-window data from MEAs, finding that PCA with k-means and Euclidean distance performs best.

This paper presents an inversion method to recover microscopic rate constants from Michaelis-Menten parameters in enzyme kinetics, with applications to biochemical pathway modeling.

This paper evaluates ROI extraction methods for MEG connectivity analysis and recommends strategies based on sensitivity and specificity trade-offs.

This study finds that the optimal regularization parameter for MEG connectivity estimation is smaller than that for source imaging and provides open-source code for analysis.

The paper uses chemical reaction networks to model the MAPK pathway in colorectal cancer, analyzing mutation effects and simulating multi-target therapy.

The paper introduces a statistical sensitivity index to assess the impact of parameters and initial concentrations on cancer-related signalling network outputs using principal component analysis.

Transfreq computes theta-to-alpha transition frequency from resting-state EEG data by clustering spectral profiles, demonstrating feasibility and robustness across datasets.