activity
20112016
most citedMinerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers

234 citations · 236 across the 6 of their papers we have counts for

collaborators

7 papers

q-bio.MN2016

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…

q-bio.MN2012★ 1 cited

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…

q-bio.MN2012★ 1 cited

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…

stat.ML2012★ 234 cited

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…

q-bio.MN2012

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…

math.CO2012

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…

q-bio.MN2011

A machine learning pipeline for discriminant pathways identification

Annalisa Barla, Giuseppe Jurman, Roberto Visintainer +4

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Re…