In recent years, numerous studies on emotion recognition have employed non-invasive electroencephalography (EEG) to measure neuronal activity. Various Machine Learning and Deep Learning techniques have been applied to classify emotions based on EEG signals. However, Graph Neural Networks (GNNs) remain relatively unexplored in this domain. Given that GNNs can accommodate dynamic and variablesized data as input, we hypothesize their potential for superior performance compared to traditional models like Support Vector Machines (SVMs) or Convolutional Neural Networks (CNNs), which rigidly fix the distance among electrodes in a pixel-like matrix. Furthermore, GNNs are designed to leverage the biological topology between different brain regions, capturing both local and global relationships among EEG channels. In this study, two GNN models are experimented with: a Dynamical Graph Convolutional Neural Network (DGCNN) and a Regularized Graph Neural Network (RGNN). Results indicate that the DGCNN model proposed in this work outperforms state-of-the-art models and the RGNN, achieving an average accuracy of 95%, compared to 53% for the latter. Additionally, the DGCNN exhibits significantly faster training times, opening up new avenues for research in this field.

Machine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data. We propose HELP (Human Gene Essentiality Labelling & Prediction), a computational framework for labelling and predicting essential genes. Its double scope allows for identifying genes based on dependency or not on experimental data. The effectiveness of the labelling method was demonstrated by comparing it with other approaches in overlapping the reference sets of essential gene annotations, where HELP demonstrated the best compromise between false and true positive rates. The gene attributes, including multi-omics and network embedding features, lead to high-performance prediction of essential genes while confirming the existence of essentiality nuances.

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec. The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.

Automatic movement-prothesis control aims to increase the quality of life for patients with diseases causing temporary or permanent paralysis or, in the worst case, the lost of limbs. This technology requires the interaction between the user and the device through a control interface that detects the user's movement intention. Basing on the Motor-Imagery theory, many researchers have explored a wide variety of Classifiers to identify patients' physiological signals from many different sources in order to detect patients' moves intentions. We here propose a novel approach relying on the use of a Weightless Neural Network-based classifier, whose design lends itself to an easy hardware implementation. Additionally, we employ a non-invasive light weight and easy donning EEG-helmet in order to provide a portable controller interface. The developed interface is connected to a robotic hand for controlling open/close actions. We compared the proposed classifier with state of the art classifiers by showing that the proposed method achieves similar performance and contemporaneously represents a viable and practicable solution due to its portability on hardware devices, which will permit its direct implementation on the helmet board.

BACKGROUND: System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models. RESULTS: The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure. CONCLUSIONS: The proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans.

In many real life scenarios, which span from domestic interactions to industrial manufacturing processes, the objects to be manipulated are non-rigid and deformable, hence, both the location of the object and its deformation have to be tracked. Different methodologies have been applied in literature, using different sensors and techniques for addressing this problem. The main contribution of this paper is to propose a Weightless Neural Network approach for non-rigid deformable object tracking. The proposed approach allows deploying an on-line training on the shape features of the object, to adapt in real-time to changes, and to partially cope with occlusions. Moreover, the use of parallel classifiers trained on the same set of images allows tracking the movements of the objects. In this work, we evaluate the filtering/segmentation performance that is a fundamental step for the correct operation of our approach, in the scenario of pizza making.

People detection and tracking are essential capabilities in human-robot interaction (HRI). Typically, a tracker performance is evaluated by measuring objective data, such as the tracking error. However, in HRI applications, human- tracking performance does not have to be evaluated by considering it as a passive sensing behavior, but as an active sensing process, where both the robot and the human are involved within-the-loop. In this context, we foresee that the robotic non-verbal feedback, such as the head movement, plays an important role in improving the system tracking performance, as well as in reducing the human effort in the interactive tracking process. In order to verify this assumption, we evaluate a tracker performance in a joint task between a human and a robot, modeled as a game, and in three different settings. We adopt common HRI performance measures, such as the robot attention demand or the human effort, to evaluate the HRI human tracking performance scaling up with respect to the used robot feedback channels.

Cellular Automata (CA) offer a promising computational model to simulate complex phenomena that are characterized by different coupled parameters that account for the interactions of their different components. In this paper we present the Cellular Automata Network (CAN) computational model and its application to the simulation of debris/flow phenomena. We carried out some experimentations developing a CAN application that implements the SCIDDICA model (Simulation through Computational Innovative methods for the Detection of Debris flow path using Interactive Cellular Automatamodel), tested on the landslide occurred in Sarno (Italy) in 1998.

Parallelizing compilers are essential tools for parallelizing old but sophisticated sequential programs.Program restructuring techniques, like loop transformations together with dependence analysis are applied to transform automatically sequential programs into parallel code.User interaction with the parallelizing process is very useful because, on massive parallel systems, small mistakes may cause large degradation on performance.In this paper we propose an interactive compiling environment, named Graphic Parallelizing Environment (GPE) ', equipped with visualization tools to join user knowledge and compiler techniques to efficiently tune the program parallelization process.GPE is oriented to the advanced users of parallel computers.The Parafrase-2 parallelizing compiler represents the core of the environment.TCL/TK is used as middleware integration language, mainly used to implement the environment components dispatcher, and the graphical components.