Social and economic variables explain COVID-19 diffusion in European regions.

At the beginning of 2020, Italy was the country with the highest number of COVID-19 cases, not only in Europe, but also in the rest of the world, and Lombardy was the most heavily hit region of Italy. The objective of this research is to understand which variables have determined the prevalence of cases in Lombardy and in other highly-affected European regions. We consider the first and second waves of the COVID-19 pandemic, using a set of 22 variables related to economy, population, healthcare and education. Regions with a high prevalence of cases are extracted by means of binary classifiers, then the most relevant variables for the classification are determined, and the robustness of the analysis is assessed. Our results show that the most meaningful features to identify high-prevalence regions include high number of hours spent in work environments, high life expectancy, and low number of people leaving from education and neither employed nor educated or trained.

LAS: a software platform to support oncological data management.

The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data quality.

Real-time analysis of physiological data to support medical applications.

This paper presents a flexible framework that performs real-time analysis of physiological data to monitor people's health conditions in any context (e.g., during daily activities, in hospital environments). Given historical physiological data, different behavioral models tailored to specific conditions (e.g., a particular disease, a specific patient) are automatically learnt. A suitable model for the currently monitored patient is exploited in the real-time stream classification phase. The framework has been designed to perform both instantaneous evaluation and stream analysis over a sliding time window. To allow ubiquitous monitoring, real-time analysis could also be executed on mobile devices. As a case study, the framework has been validated in the intensive care scenario. Experimental validation, performed on 64 patients affected by different critical illnesses, demonstrates the effectiveness and the flexibility of the proposed framework in detecting different severity levels of monitored people's clinical situations.

Minimum number of genes for microarray feature selection.

A fundamental problem in microarray analysis is to identify relevant genes from large amounts of expression data. Feature selection aims at identifying a subset of features for building robust learning models. However, finding the optimal number of features is a challenging problem, as it is a trade off between information loss when pruning excessively and noise increase when pruning is too weak. This paper presents a novel representation of genes as strings of bits and a method which automatically selects the minimum number of genes to reach a good classification accuracy on the training set. Our method first eliminates redundant features, which do not add further information for classification, then it exploits a set covering algorithm. Preliminary experimental results on public datasets confirm the intuition of the proposed method leading to high classification accuracy.

The painter's feature selection for gene expression data.

Feature selection is a fundamental task in microarray data analysis. It aims at identifying the genes which are mostly associated with a tissue category, disease state or clinical outcome. An effective feature selection reduces computation costs and increases classification accuracy. This paper presents a novel multi-class approach to feature selection for gene expression data, which is called Painter's approach. It has the benefits of both a parameter free technique and a native multicategory method. It consists of two phases. The first is a filtering phase that smooths the effect of noise and outliers, which represent a common problem in microarray data. In the second phase, the actual gene selection is performed. Preliminary experimental results on three public datasets are presented. They confirm the intuition of the proposed approach leading to high classification accuracies.