SOMMARIVA, SARA
SOMMARIVA, SARA
100021 - Dipartimento di Matematica
A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length
2019-01-01 Sommariva, Sara; Sorrentino, Alberto; Piana, Michele; Pizzella, Vittorio; Marzetti, Laura
A simplex method for the calibration of a MEG device
2019-01-01 Vivaldi, V.; Sommariva, S.; Sorrentino, A.
Bayesian multi-dipole modelling in the frequency domain
2019-01-01 Luria, G.; Duran, D.; Visani, E.; Sommariva, S.; Rotondi, F.; Rossi Sebastiano, D.; Panzica, F.; Piana, M.; Sorrentino, A.
Combined Newton-Gradient Method for Constrained Root-Finding in Chemical Reaction Networks
2023-01-01 Berra, S; La Torraca, A; Benvenuto, F; Sommariva, S
Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells
2021-01-01 Sommariva, S.; Caviglia, G.; Ravera, S.; Frassoni, F.; Benvenuto, F.; Tortolina, L.; Castagnino, N.; Parodi, S.; Piana, M.
Editorial: Data science in neuro- and onco-biology
2023-01-01 Sommariva, S; Subramaniyam, Np; Piana, M
EEG in extreme conditions: An advanced analysis pipeline for the human electroencephalographic signals recorded in space during the ALTEA experiment
2023-01-01 Sommariva, Sara; Romoli, Giulia; Vallarino, Elisabetta; di Fino, Luca; Sorrentino, Alberto; Santi Amantini, Giorgia; Sannita, Walter G.; Piana, Michele; Narici, Livio
Gain and loss of function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells
2021-01-01 Sommariva, S.; Caviglia, G.; Piana, M.
In-silico modelling of the mitogen-activated protein kinase (MAPK) pathway in colorectal cancer: mutations and targeted therapy
2023-01-01 Sommariva, Sara; Berra, Silvia; Biddau, Giorgia; Caviglia, Giacomo; Benvenuto, Federico; Piana, Michele
Mathematical models for fdg kinetics in cancer: A review
2021-01-01 Sommariva, S.; Caviglia, G.; Sambuceti, G.; Piana, M.
On the two-step estimation of the cross-power spectrum for dynamical linear inverse problems
2020-01-01 Vallarino, E.; Sommariva, S.; Piana, M.; Sorrentino, A.
PCA-based synthetic sensitivity coefficients for chemical reaction network in cancer
2024-01-01 Biddau, Giorgia; Caviglia, Giacomo; Piana, Michele; Sommariva, Sara
Sequential Monte Carlo samplers for semi-linear inverse problems and application to magnetoencephalography
2014-01-01 Sommariva, Sara; Sorrentino, Alberto
The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data
2023-01-01 Brkic, D; Sommariva, S; Schuler, Al; Pascarella, A; Belardinelli, P; Isabella, Sl; Di Pino, G; Zago, S; Ferrazzi, G; Rasero, J; Arcara, G; Marinazzo, D; Pellegrino, G
The role of endoplasmic reticulum in in vivo cancer FDG kinetics
2021-01-01 Sommariva, S.; Scussolini, M.; Cossu, V.; Marini, C.; Sambuceti, G.; Caviglia, G.; Piana, M.
The role of spectral complexity in connectivity estimation
2021-01-01 Vallarino, E.; Sorrentino, A.; Piana, M.; Sommariva, S.
The SESAMEEG package: a probabilistic tool for source localization and uncertainty quantification in M/EEG
2024-01-01 Luria, G.; Viani, A.; Pascarella, A.; Bornfleth, H.; Sommariva, S.; Sorrentino, A.
Therapeutic Implications of Tumor Microenvironment in Lung Cancer: Focus on Immune Checkpoint Blockade
2022-01-01 Genova, C.; Dellepiane, C.; Carrega, P.; Sommariva, S.; Ferlazzo, G.; Pronzato, P.; Gangemi, R.; Filaci, G.; Coco, S.; Croce, M.
Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data
2022-01-01 Vallarino, E.; Sommariva, S.; Fama, F.; Piana, M.; Nobili, F.; Arnaldi, D.
Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation
2023-01-01 Vallarino, E.; Hincapie, A. S.; Jerbi, K.; Leahy, R. M.; Pascarella, A.; Sorrentino, A.; Sommariva, S.
Titolo | Data di pubblicazione | Autore(i) | File |
---|---|---|---|
A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length | 1-gen-2019 | Sommariva, Sara; Sorrentino, Alberto; Piana, Michele; Pizzella, Vittorio; Marzetti, Laura | |
A simplex method for the calibration of a MEG device | 1-gen-2019 | Vivaldi, V.; Sommariva, S.; Sorrentino, A. | |
Bayesian multi-dipole modelling in the frequency domain | 1-gen-2019 | Luria, G.; Duran, D.; Visani, E.; Sommariva, S.; Rotondi, F.; Rossi Sebastiano, D.; Panzica, F.; Piana, M.; Sorrentino, A. | |
Combined Newton-Gradient Method for Constrained Root-Finding in Chemical Reaction Networks | 1-gen-2023 | Berra, S; La Torraca, A; Benvenuto, F; Sommariva, S | |
Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells | 1-gen-2021 | Sommariva, S.; Caviglia, G.; Ravera, S.; Frassoni, F.; Benvenuto, F.; Tortolina, L.; Castagnino, N.; Parodi, S.; Piana, M. | |
Editorial: Data science in neuro- and onco-biology | 1-gen-2023 | Sommariva, S; Subramaniyam, Np; Piana, M | |
EEG in extreme conditions: An advanced analysis pipeline for the human electroencephalographic signals recorded in space during the ALTEA experiment | 1-gen-2023 | Sommariva, Sara; Romoli, Giulia; Vallarino, Elisabetta; di Fino, Luca; Sorrentino, Alberto; Santi Amantini, Giorgia; Sannita, Walter G.; Piana, Michele; Narici, Livio | |
Gain and loss of function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells | 1-gen-2021 | Sommariva, S.; Caviglia, G.; Piana, M. | |
In-silico modelling of the mitogen-activated protein kinase (MAPK) pathway in colorectal cancer: mutations and targeted therapy | 1-gen-2023 | Sommariva, Sara; Berra, Silvia; Biddau, Giorgia; Caviglia, Giacomo; Benvenuto, Federico; Piana, Michele | |
Mathematical models for fdg kinetics in cancer: A review | 1-gen-2021 | Sommariva, S.; Caviglia, G.; Sambuceti, G.; Piana, M. | |
On the two-step estimation of the cross-power spectrum for dynamical linear inverse problems | 1-gen-2020 | Vallarino, E.; Sommariva, S.; Piana, M.; Sorrentino, A. | |
PCA-based synthetic sensitivity coefficients for chemical reaction network in cancer | 1-gen-2024 | Biddau, Giorgia; Caviglia, Giacomo; Piana, Michele; Sommariva, Sara | |
Sequential Monte Carlo samplers for semi-linear inverse problems and application to magnetoencephalography | 1-gen-2014 | Sommariva, Sara; Sorrentino, Alberto | |
The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data | 1-gen-2023 | Brkic, D; Sommariva, S; Schuler, Al; Pascarella, A; Belardinelli, P; Isabella, Sl; Di Pino, G; Zago, S; Ferrazzi, G; Rasero, J; Arcara, G; Marinazzo, D; Pellegrino, G | |
The role of endoplasmic reticulum in in vivo cancer FDG kinetics | 1-gen-2021 | Sommariva, S.; Scussolini, M.; Cossu, V.; Marini, C.; Sambuceti, G.; Caviglia, G.; Piana, M. | |
The role of spectral complexity in connectivity estimation | 1-gen-2021 | Vallarino, E.; Sorrentino, A.; Piana, M.; Sommariva, S. | |
The SESAMEEG package: a probabilistic tool for source localization and uncertainty quantification in M/EEG | 1-gen-2024 | Luria, G.; Viani, A.; Pascarella, A.; Bornfleth, H.; Sommariva, S.; Sorrentino, A. | |
Therapeutic Implications of Tumor Microenvironment in Lung Cancer: Focus on Immune Checkpoint Blockade | 1-gen-2022 | Genova, C.; Dellepiane, C.; Carrega, P.; Sommariva, S.; Ferlazzo, G.; Pronzato, P.; Gangemi, R.; Filaci, G.; Coco, S.; Croce, M. | |
Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data | 1-gen-2022 | Vallarino, E.; Sommariva, S.; Fama, F.; Piana, M.; Nobili, F.; Arnaldi, D. | |
Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation | 1-gen-2023 | Vallarino, E.; Hincapie, A. S.; Jerbi, K.; Leahy, R. M.; Pascarella, A.; Sorrentino, A.; Sommariva, S. |