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Disease comorbidity is a result of complex epigenetic interplay. A disease is rarely a consequence of an abnormality in a single gene; complex pathways to disease patterns emerge from gene-gene interactions and gene-environment interactions. Understanding these mechanisms of disease and comorbidity development, breaking down them into clusters and disentangling the epigenetic - actionable - components, is of utter importance from a public health perspective. With the increase in the average life expectancy, healthy aging becomes a primary objective, from both an individual (i.e. quality of life) and a societal (i.e. healthcare costs) standpoint. Many studies have analyzed disease networks based on common altered genes, on protein-protein interactions, or on shared disease comorbidites, i.e. phenotypic disease networks. In this work we aim at studying the relations between genotypic and phenotypic disease networks, using a large statewide cohort of individuals (100, 000+ from California, USA) with linked clinical and genotypic information, the Genetic Epidemiology Research on Adult Health and Aging (GERA). By comparing their phenotypic and genotypic networks, we try to disentangle the epigenetic component of disease comorbidity.

Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient’s quality of life.

Diagnostic and clinical support is performed by using ever improving DICOM image systems. Huge quantity of clinical images are generated from clinical devices with increasing high performance. Nevertheless, extracting useful information from DICOM images for diagnosis as well as integrating information from different data sources is still a complex task. We present eIMES 3D (standing for Evolution Imaging System 3D), a system that supports clinicians for images studies, diagnostic and images reconstruction. The tool has been developed within a project called ReCaTuR for RAre Cancer Network (i.e., Network of Rare Cancer), aiming to define a network for the management, organization and distribution of medical information. Moreover, it was implemented following the requirements of oncology department of an Italian Hospital. By using eIMES 3D a cancer network data infrastructure has been defined and implemented aiming to integrate information regarding rare and complex diseases. Data provided by different departments, external structures and research institutes are used to improve knowledge and to support physicians. eIMES 3D allows (i) full control and management of the data by means of artificial intelligence algorithms; (ii) advanced stereoscopic 3D visualization by using the WebGL innovative technology; (iii) sharing of medical data; (iv) distribution of 3D imaging on different output devices (web, TV, mobile); (v) querying the system through a search of the various case studies.

The concept of “neural network” emerges by electronic models inspired to the neural structure of human brain. Neural networks aim to solve problems currently out of computer's calculation capacity, trying to mimic the role of human brain. Recently, the number of biological based applications using neural networks is growing up. Biological networks represent correlations, extracted from sets of clinical data, diseases, mutations, and patients, and many other types of clinical or biological features. Biological networks are used to model both the state of a range of functionalities in a particular moment, and the space-time distribution of biological and clinical events. The study of biological networks, their analysis and modeling are important tasks in life sciences. Most biological networks are still far from being complete and they are often difficult to interpret due to the complexity of relationships and the peculiarities of the data. Starting from preliminary notions about neural networks, we focus on biological networks and discuss some well-known applications, like protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network, phylogenetic trees and special networks. Finally, we consider the use of biological network inside a proposed model to map health related data.

The integration of clinical and geographical data of patients is a recent trend in bio and medical informatics since such integration may reveal hidden association association patterns among patients. For instance, the presence of the same disease or the pattern of changes in biological analytes in patients of the different geographical areas may suggest the presence of possible environmental factors that affect patients health. The joint analysis of patients considering both analytical parameters and geographical features requires the use of appropriate models and algorithms to integrate and mine such data. A possibility could be the use of the dual network model that has been used recently in bioinformatic to model many interesting biological scenarios. A dual network is a pair of related graph sharing a subset of nodes and edges. Usually one of the two graph is unweighted (a physical network representing physical association among nodes) while the other one is edge-weighted (a conceptual network representing conceptual association among nodes). In such a way related subset of nodes are connected in the physical network and the induced subgraph in the conceptual network has a high density. In such a model the the problem of finding the densest connected subgraph (DCS) which has the largest density in the conceptual network and is also connected in the physical network is relevant. In our approach we model patients as nodes in both networks, while the physical network models the geographical association among them (i.e. patients living in the same area or in areas with same environmental factors), while the conceptual network represents the association among patients considering similar patterns of biological analytes (for instance patients having a common mutation, or patients having a similar pattern of changes in analytes and the strenght of the association is represented by the weight of the nodes). Then we mine both networks searching for DCS. In our representation a DCS is a group of geographically related patients having a similar pattern of expression. The DCS problem is NP-hard, thus the need for the introduction of reliable solution arises. Here we formalize the problem and then we map the DCS problem in to a graph alignment problem. Then we propose a possible solution. Our findings are currently under the validation of the clinicians.

Clinical records (also known as Electronic Medical Records or EMRs) have been considered as a collection of patients health information. Elettronic Citizen Helth Records store information regarding life style, activities, habits, environment (i.e. water sources or air quality), routine health screening like blood biological analysis. Patients and providers stand to benefit from Internet of Things (IoT) in healthcare. Some uses of healthcare IoT are mobile medical applications or wearable devices that allow patients to capture their health data. Smart personal devices as well as data generator devices (i.e., internet of things) allow to provide such kind of data which may be collected in unique (cloud) database. Such information can then be related to location data within a geographic context. We use clinical anonymized data extracted from a Biological Department of University Magna Graecia Hospital. We show how to include geographic operators to statistical methods and how to analyze environmental data and citizen habits to improve wellness. We report on developing and testing a geo-based system to analyze biological data.

Personalized medicine is the new horizon of the medical science. Its main goal is to improve the quality of patient care, both in prevention and in therapeutic stage, and to improve the precision of therapy through the integrated analysis of clinical, biological and molecular data. Data integration represents a powerful instrument for clinicians to have an overall vision of diseases. Even if clinical data integration has been treated in many recent papers, few results have been presented with respect to integrating proteomics and genomics data. We present the architecture for the integration of genetic and phenotype data extracted from medical records. The focus is information extraction and data prefiling for early detection of chronic diseases. Focus is about cancer diseases where omics data, environmental, ontologies and clinical data can be integrated to improve knowledge about the risk assessment and genetic susceptibility.

Sleep apnoea syndrome (SAS) is a disease consisting in the nocturnal cessation of oronasal airflow at least 10 seconds in duration. The standard method for SAS diagnosis is the polysomnographic exam (PSG). However it does not permit a mass screening because it has high cost and requires long term monitoring. This paper presents a preliminary software system prototype for snoring signal analysis, whose main goal is to support the doctor in SAS diagnosis and patient follow-up. The design of the system is modular to allow a future hardware implementation in a portable device for personal snore collection and monitoring.