3 citations · 3 across the 7 of their papers we have counts for
7 papers
Efficient Shapley values computation for Boolean network models of gene regulation
Giang Pham, Silvia Giulia Galfrè, Paolo Milazzo
Identifying dynamically influential nodes in biological networks is a central problem in systems biology, particularly for prioritizing intervention targets in gene regulatory netw…
MIEO: encoding clinical data to enhance cardiovascular event prediction
Davide Borghini, Davide Marchi, Angelo Nardone +6
As clinical data are becoming increasingly available, machine learning methods have been employed to extract knowledge from them and predict clinical events. While promising, appro…
A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
Samuele Punzo, Silvia Giulia Galfrè, Francesco Massafra +3
We present a machine learning pipeline for biomarker discovery in Multiple Sclerosis (MS), integrating eight publicly available microarray datasets from Peripheral Blood Mononuclea…
Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024
Carlo Metta, Marco Gregnanin, Andrea Papini +5
This paper presents the second-place methodology in the Volvo Discovery Challenge at ECML-PKDD 2024, where we used Long Short-Term Memory networks and pseudo-labeling to predict ma…
A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement Learning
Flora Angileri, Giulia Lombardi, Andrea Fois +9
In 2021, Adam Zsolt Wagner proposed an approach to disprove conjectures in graph theory using Reinforcement Learning (RL). Wagner's idea can be framed as follows: consider a conjec…
Increasing biases can be more efficient than increasing weights
Carlo Metta, Marco Fantozzi, Andrea Papini +7
We introduce a novel computational unit for neural networks that features multiple biases, challenging the traditional perceptron structure. This unit emphasizes the importance of…
A mathematical framework for raw counts of single-cell RNA-seq data analysis
Silvia Giulia Galfre', Francesco Morandin
Single-cell RNA-seq data are challenging because of the sparseness of the read counts, the tiny expression of many relevant genes, and the variability in the efficiency of RNA extr…