Multiomics is an emerging biological analysis approach in which the datasets come from multiple “omics”, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. Nowadays, the convergence of Deep Learning and multiomics sciences presents an unprecedented opportunity to dissect the intricate interplay of biological processes. Specifically, multiomics data integration, propelled by Deep Learning methodologies, has revolutionised biological research, enabling a more holistic understanding of complex biological systems and disease mechanisms. This paper explores the current landscape of Deep Learning applications in multiomics, highlighting state-of-the-art techniques, emerging research areas, and the challenges that lie ahead. In particular, we delve into the application areas and computational methods that have been considered so far, offering guidance to researchers navigating this intricate field.

Remote patient monitoring is a form of telehealth that allows medical centres to monitor and manage their patients’ chronic conditions. Often, depending on the severity of the disease, patients can experience either temporary or permanent home hospitalization. Although the classical medical approach involves continuous monitoring of vital parameters through specialized medical devices, it does not allow observation of patient’s behaviours, which may provide additional information of interest to physicians. In this context, digital biomarkers represent the next frontiers towards precision medicine. In this paper, we explore the possible adoption of Audio Biomarkers for monitoring the behaviours of long-term home hospitalized patients. In particular, we trained and tested several Machine Learning (ML) models to recognise different sounds (i.e., sneezing, breathing, coughing, snoring, teeth brushing, and toilet flush). The results show that, even with a few audio samples, the considered models provide good performance.

In the last few years, the healthcare industry has seen significant advances in medical image analysis, mainly driven by the substantial progress of Deep Learning (DL). Convolutional Neural Networks (CNNs) have been the reference model for image-processing tasks. Recently, however, the advent of Vision Transformers (ViTs) has challenged their dominance. In this work, we explore the potential of ViTs for pneumonia diagnosis, comparing their performance with CNNs using different learning approaches. Specifically, we assessed the behaviour of From-Scratch Learning (FSL) and Pre-Trained (PT) models, leveraging Transfer Learning (TL), to highlight their performance differences. Experiments are performed in a Microsoft Azure Cloud laboratory considering both centralised and distributed Federated Learning (FL) scenarios, proving that the latter helps to mitigate the potential biases contained in the dataset, achieving similar accuracies and reducing training times linearly with the number of clients.

Tele-rehabilitation has recently emerged as an effective solution for providing assisted living, increasing clinical outcomes, positively enhancing patients' Quality of Life (QoL) and fostering their reintegration into society, also pushing down clinical costs. Cloud computing in combination with Edge Computing, the Internet of Things (IoT), Big Data storage and analytics, and Artificial Intelligence (AI) are the main enablers for tele-rehabilitation. In this paper, we present the Italian founded PRIN 2022 project entitled “Tele-rehabilitation as a Service (TRaaS)”. It aims at creating a piece of reference intelligent Cloud/Edge framework architecture and a standard data model for the development of different kinds of new de-hospitalized tele-rehabilitation services. In particular, the rationale, study design, and methodology are discussed, also highlighting future research directions.

Nowadays, tele-rehabilitation has emerged as an effective approach for providing assisted living, increasing clinical outcomes, positively enhancing patients' Quality of Life (QoL) and fostering the reintegration of patients into society, also pushing down clinical costs. Cloud computing in combination with Edge Computing and Artificial Intelligence (AI) are the main enablers for tele-rehabilitation. In particular, Edge rehabilitation devices can act as smart digital biomarkers sending quantifiable physiological and behavioural patients' data to the Hospital Cloud. However, due to hardware limitations, it is not clear which Machine Learning (ML) models can be executed in cheap Edge devices. In this paper, we aim at answering this question. In particular, several ML-based pose estimation models (i.e., PoseNet, MoveNet and BlazePose) have been tested and assessed on the Edge, identifying the best one and demonstrating the feasibility of such an approach.

Tele-biomedical laboratory is a medical laboratory where blood exams are performed either by patients themselves in their homes or by biomedical technicians in satellite clinical centres through the Internet of Things (IoT) biomedical devices interconnected with Hospital Edge/Cloud systems that allow results to be sent to doctors belonging to federated hospitals for validation and/or consultation. This paper aims at providing a clear picture of the current state of the art in the tele- biomedical laboratory, also highlighting current issues and future challenges. Specifically, we start motivating the need for tele-biomedical laboratories adopting IoT, Edge and Cloud technologies. After a classification of the main biomedical equipment (considering connected, not-connected, invasive, minimally invasive and noninvasive devices), we present different possible tele-biomedical laboratory scenarios. In the end, we will discuss the recent issues, current feasibility and future challenges.

Recent advancements in ICT have sped up the development of new services in healthcare. In this context, remote patient monitoring and rehabilitation activities can take place either in satellite hospital centers or directly in patients’ homes. Specifically, using a combination of Cloud/Edge computing, Internet of Things (IoT) and Machine Learning (ML) technologies, patients with motor disabilities can be remotely assisted avoiding stressful waiting times and overcoming geographical barriers. This is possible by applying the Tele-Rehabilitation as a Service (TRaaS) concept. The objective of this paper is twofold: i) studying how Machine Learning can improve the TRaaS, and ii) demonstrating how a NoSQL graph database approach can enhance the performance because it works directly at the database layer instead of at application one. In particular, the K-Nearest Neighbors (K-NN) algorithm is studied in order to identify the best therapy, i.e., rehabilitation training, for a new remote patient with motor impairment. Experiments compare two system prototypes, that are respectively based on Python and Neo4j, showing that the latter presents better performance in terms of processing time guaranteeing the same accuracy.

The study of DNA sequences has become indis-pensable for basic biological research, and in numerous applied fields such as comparative genomics, evolutionary biology, pan genomics, genetics of disease, regulation of gene expression, oncology and many others, all supported by bioinformatics. In the era of Cloud computing, federating the Cloud systems of different genetics research organisations paves the way towards a new era of data sharing and new mashup services and applications. However, due to the huge amount of genomics data (genomics Big Data) that have to be managed, a parallel distributed NoSQL DataBase Management System (DBMS) approach becomes fundamental. Specifically, due to the textual nature of genomics data, a NoSQL DBMS appears to be the most suitable solution. In this paper, by considering the whole human genome, we present a preliminary study comparing this latter using MongoDB with a SQL-like database solution, i.e., MySQL in order to look for DNA sequences. Moreover, in order to optimize the research of genomics codes, we adopt hash functions that allow mapping nucleotides sequences of arbitrary size onto data of a fixed smaller size. Experiments, shows that MongoDB apart simplifying the management of genomics data provides better performances.

Nowadays, Healthcare Social Networks (HSNs) offer the possibility to enhance patient care and education. However, they also present potential risks for users due to the possible distribution of poor-quality or wrong information along with their bad interpretation. In recent years several discordant information have been diffused in social networks regarding potential risks of flu vaccines. In this paper, by considering a Twitter datasets, we study the accuracy of users' opinions comparing different Machine Learning approaches including Bayesian, Linear and Support Vector Machine (SVM) classifiers.

Nowadays, Next Generation Sequeencing (NGS) is a catch-all term used to describe different modern DNA sequencing applications that produce big genomics data that can be analysed in a faster fashion than in the past. For this reason, NGS requires more and more sophisticated algorithms and high-performance parallel processing systems able to analyse and extract knowledge from a huge amount of genomics and molecular data. In this context, researchers are beginning to look at emerging deep learning algorithms able to perform efficient big data analytics. In this paper, we analyse and classify the major current deep learning solutions that allow biotechnology researchers to perform big genomics data analytics. Moreover, by means of a taxonomic analysis, we provide a clear picture of the current state of the art also discussing future challenges.

The advent of Cloud computing is changing the way of conceiving information and communication systems in different application fields including Biotechnology. In this context, an emerging research field is Next-Generation Sequencing (NGS) that includes several recent technologies allowing sequencing DNA and that have revolutionized the study of genomics and molecular biology. These cutting-edge sequencing systems produce big datasets that require significant scalable computing resources. In this paper, we analyse and classify the major current NGS Cloud-based solutions adopted in scientific laboratories according to different Cloud service levels. Moreover, by means of a taxonomy, we discuss the challenges and advantages of possible future NGS Cloud-based systems.