419 citations · 1k across the 27 of their papers we have counts for
30 papers
Exploring Physiological Responses in Virtual Reality-based Interventions for Autism Spectrum Disorder: A Data-Driven Investigation
Gianpaolo Alvari, Ersilia Vallefuoco, Melanie Cristofolini +6
Virtual Reality (VR) has emerged as a promising tool for enhancing social skills and emotional well-being in individuals with Autism Spectrum Disorder (ASD). Through a technical ex…
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee +6
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data pr…
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive
Gabriele Franch, Giuseppe Jurman, Luca Coviello +2
The use of analogs - similar weather patterns - for weather forecasting and analysis is an established method in meteorology. The most challenging aspect of using this approach in…
In-field grape berries counting for yield estimation using dilated CNNs
L. Coviello, M. Cristoforetti, G. Jurman +1
Digital technologies ignited a revolution in the agrifood domain known as precision agriculture: a main question for enabling precision agriculture at scale is if accurate product…
High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning
Andrea Gobbi, Marco Cristoforetti, Giuseppe Jurman +1
Climate change impacts could cause progressive decrease of crop quality and yield, up to harvest failures. In particular, heat waves and other climate extremes can lead to localize…
AI slipping on tiles: data leakage in digital pathology
Nicole Bussola, Alessia Marcolini, Valerio Maggio +2
Reproducibility of AI models on biomedical data still stays as a major concern for their acceptance into the clinical practice. Initiatives for reproducibility in the development o…
A multiobjective deep learning approach for predictive classification in Neuroblastoma
Valerio Maggio, Marco Chierici, Giuseppe Jurman +1
Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical courses that may vary from spontaneous regression to fatal progression; an accurate patient's risk estim…
Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer
Giuseppe Jurman, Valerio Maggio, Diego Fioravanti +7
Convolutional Neural Networks (CNNs) are a popular deep learning architecture widely applied in different domains, in particular in classifying over images, for which the concept o…
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders
Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo +5
Autism Spectrum Disorders are associated with atypical movements, of which stereotypical motor movements (SMMs) interfere with learning and social interaction. The automatic SMM de…
Phylogenetic Convolutional Neural Networks in Metagenomics
Diego Fioravanti, Ylenia Giarratano, Valerio Maggio +4
Background: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels…
Towards a scientific blockchain framework for reproducible data analysis
C. Furlanello, M. De Domenico, G. Jurman +1
Publishing reproducible analyses is a long-standing and widespread challenge for the scientific community, funding bodies and publishers. Although a definitive solution is still el…
Towards meaningful physics from generative models
Marco Cristoforetti, Giuseppe Jurman, Andrea I. Nardelli +1
In several physical systems, important properties characterizing the system itself are theoretically related with specific degrees of freedom. Although standard Monte Carlo simulat…
Differential network analysis and graph classification: a glocal approach
Giuseppe Jurman, Michele Filosi, Samantha Riccadonna +2
Based on the glocal HIM metric and its induced graph kernel, we propose a novel solution in differential network analysis that integrates network comparison and classification task…
Community dynamics in connected time-dependent multilayer networks
Marco Cristoforetti, Marco Guerini, Giuseppe Jurman +1
Different strategies have been considered to extract information from social media about how similarly people react to the same news or event. In this context, a powerful method is…
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism
Nastaran Mohammadian Rad, Andrea Bizzego, Seyed Mostafa Kia +3
Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility.…
Entropy Dynamics of Community Alignment in the Italian Parliament Time-Dependent Network
Gabriele Lami, Marco Cristoforetti, Giuseppe Jurman +2
Complex institutions are typically characterized by meso-scale structures which are fundamental for the successful coordination of multiple agents. Here we introduce a framework to…
Sparse Predictive Structure of Deconvolved Functional Brain Networks
Tommaso Furlanello, Marco Cristoforetti, Cesare Furlanello +1
The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional stru…
DTW-MIC Coexpression Networks from Time-Course Data
Samantha Riccadonna, Giuseppe Jurman, Roberto Visintainer +2
When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. Howe…
Stability Indicators in Network Reconstruction
Giuseppe Jurman, Michele Filosi, Roberto Visintainer +2
The number of algorithms available to reconstruct a biological network from a dataset of high-throughput measurements is nowadays overwhelming, but evaluating their performance whe…
Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers
Davide Albanese, Michele Filosi, Roberto Visintainer +3
We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large dataset…
mlpy: Machine Learning Python
Davide Albanese, Roberto Visintainer, Stefano Merler +3
mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning…
Evaluating sources of variability in pathway profiling
A. Barla, S. Riccadonna, S. Masecchia +4
A bioinformatics platform is introduced aimed at identifying models of disease-specific pathways, as well as a set of network measures that can quantify changes in terms of global…
The HIM glocal metric and kernel for network comparison and classification
Giuseppe Jurman, Roberto Visintainer, Michele Filosi +2
Due to the ever rising importance of the network paradigm across several areas of science, comparing and classifying graphs represent essential steps in the networks analysis of co…
Single-base mismatch profiles for NGS samples
Marco Chierici, Giuseppe Jurman, Marco Roncador +1
Within the preprocessing pipeline of a Next Generation Sequencing sample, its set of Single-Base Mismatches is one of the first outcomes, together with the number of correctly alig…