In this article Andrea Pugnana explores selective classification in machine learning, introducing a novel heuristic for improving classifier performance. He also discusses the challenges of performance metrics and highlights future research directions.

The predictive performance of classifiers is typically not homogeneous over the data distribution. This is a common issue in Machine Learning. In fact, identifying sub-populations with low performance could be helpful, e.g., for debugging and monitoring purposes, especially in high-risk scenarios.

Introduction

Proper risk stratification of patients with differentiated thyroid cancer (DTC) is essential to avoid both unnecessary diagnostic procedures in low-risk patients and clinical inertia in cases that may require more aggressive treatment (1).

-A TNA experience-

Author: Michele Gentili.

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by environmental exposures (most notably, cigarette smoking) and genetic factors. Genome-wide association studies have identified thousands of genomic regions associated with complex diseases. The chromosome 4q region harbors multiple genetic risk loci for chronic obstructive pulmonary disease (COPD). To determine whether genes in this region are part of a gene expression network, we studied lung tissue RNA-Seq from COPD cases and controls.  

Author: Leonardo Martini, Sapienza University of Rome

Protein-Protein Interactions (PPIs) play an essential role in several biological processes. In many cases, proteins perform essential functions by interacting to constitute protein complexes. Identifying new Protein-Protein interactions is thus crucial in understanding cells' biological mechanisms.

Authored by: Guglielmo Cola (CNR-IIT)

Charging Data Records are acknowledged as a standard tool for studying human mobility, infrastructure usage, and traffic behavior. We name such datasets as CdRs to distinguish them from the traditional Call Detail Records (CDRs), describing call and SMS cellular communication only. CdRs describe time-stamped and geo-referenced event types (i.e., data, calls, SMS) generated by each mobile device interacting with operator networks.

Social networks enable us to stay in contact with people all over the world, and to read news related to events that occur over the globe in almost real-time. While this has many positive impacts, such as allowing the report of adverse events. Unfortunately, such speed of diffusion of information over social networks has also important negative implications. In many cases malicious users may try to take advantage of the structure of social networks to spread misinformation.

An exploratory study of infodemic around the pandemic

Authors: Shakshi Sharma, Rajesh Sharma (University of Tartu, Estonia)

The ongoing discourse in social media has amplified the fears, uncertainties, and doubts (FUD) surrounding COVID-19 and the currently available vaccines, leading to an infodemic (a portmanteau of information and epidemic, referring to the spread of potentially accurate and inaccurate information about a disease spreading like an epidemic).