37 citations · 61 across the 12 of their papers we have counts for
14 papers
A neighbour selection approach for identifying differential networks in conditional functional graphical models
Alessia Mapelli, Laura Carini, Francesca Ieva +1
Estimating how different brain regions communicate with each other using EEG data is valuable both for medical research and clinical diagnosis. This involves quantifying the statis…
Testing for a common subspace in compositional datasets with structural zeros
Francesco Porro, Fabio Rapallo, Sara Sommariva
In this paper, we consider the problem of testing for a common principal subspace in compositional datasets with structural zeros. In particular, we address the problem in the gene…
AI-based modular warning machine for risk identification in proximity healthcare
Chiara Razzetta, Shahryar Noei, Federico Barbarossa +17
"DHEAL-COM - Digital Health Solutions in Community Medicine" is a research and technology project funded by the Italian Department of Health for the development of digital solution…
When algebra twinks system biology: a conjecture on the structure of Gröbner bases in complex chemical reaction networks
Paola Ferrari, Sara Sommariva, Michele Piana +2
We address the challenge of identifying all real positive steady states in chemical reaction networks (CRNs) governed by mass-action kinetics. Traditional numerical methods often r…
Sparse optimization for estimating the cross-power spectrum in linear inverse models : from theory to the application in brain connectivity
Laura Carini, Isabella Furci, Sara Sommariva
In this work we present a computationally efficient linear optimization approach for estimating the cross--power spectrum of an hidden multivariate stochastic process from that of…
A fast and convergent combined Newton and gradient descent method for computing steady states of chemical reaction networks
Silvia Berra, Alessandro La Torraca, Federico Benvenuto +1
In this work we present a fast, globally convergent, iterative algorithm for computing the asymptotically stable states of nonlinear large--scale systems of quadratic autonomous Or…
The role of spectral complexity in connectivity estimation
Elisabetta Vallarino, Michele Piana, Alberto Sorrentino +1
The study of functional connectivity from magnetoecenphalographic (MEG) data consists in quantifying the statistical dependencies among time series describing the activity of diffe…
Gain and Loss of Function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells
Sara Sommariva, Giacomo Caviglia, Michele Piana
This paper studies a system of Ordinary Differential Equations modeling a chemical reaction network and derives from it a simulation tool mimicking Loss of Function and Gain of Fun…
Mathematical modelling of nuclear medicine data
Michele Piana, Giacomo Caviglia, Sara Sommariva
Positron Emission Tomography using 2-[18F]-2deoxy-D-glucose as radiotracer (FDG-PET) is currently one of the most frequently applied functional imaging methods in clinical applicat…
On the two-step estimation of the cross--power spectrum for dynamical inverse problems
Elisabetta Vallarino, Sara Sommariva, Michele Piana +1
We consider the problem of reconstructing the cross--power spectrum of an unobservable multivariate stochatic process from indirect measurements of a second multivariate stochastic…
Bayesian Multi--Dipole Modeling in the Frequency Domain
Gianvittorio Luria, Dunja Duran, Elisa Visani +6
Background: Magneto- and Electro-encephalography record the electromagnetic field generated by neural currents with high temporal frequency and good spatial resolution, and are the…
A comparative study of the robustness of frequency--domain connectivity measures to finite data length
Sara Sommariva, Alberto Sorrentino, Michele Piana +2
In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time--series. We…
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings
Filippo Rossi, Gianvittorio Luria, Sara Sommariva +1
We deal with estimation of multiple dipoles from combined MEG and EEG time--series. We use a sequential Monte Carlo algorithm to characterize the posterior distribution of the numb…
Sequential Monte Carlo samplers for semilinear inverse problems and application to magnetoencephalography
Sara Sommariva, Alberto Sorrentino
We discuss the use of a recent class of sequential Monte Carlo methods for solving inverse problems characterized by a semi-linear structure, i.e. where the data depend linearly on…