Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further

Operating an eHealth System for Prehospital and Emergency Health Care Support in Light of Covid-19

Introduction: The support of prehospital and emergency call handling and the impact of Covid-19 is discussed throughout this study. The initial purpose was to create an electronic system (eEmergency system) in order to support, improve, and help the procedure of handling emergency calls. This system was expanded to facilitate needed operation changes for Covid-19. Materials and Methods: An effort to reform the procedures followed for emergency call handling and Ambulance dispatch started on the Island of Cyprus in 2016; along that direction, a central call centre was created. The electronic system presented in this work was designed for this call centre and the new organization of the ambulance services. The main features are the support for ambulance fleet handling, the support for emergency call evaluation and triage procedure, and the improvement of communication between the call centre and the ambulance vehicles. This system started regular operation at the end of 2018. One year later, when Covid-19 period started, we expanded it with the addition of several new features in order to support the handling of patients infected with the new virus. Results: This system has handled 112,414 cases during the last 25 months out of which 4,254 were Covid-19 cases. These cases include the transfer of patients from their house to the reference hospital, or the transfer of critical patients from the reference hospital to another hospital with an intensive care unit or transfer of patients from one hospital to another one for other reasons, like the number of admissions. Conclusion: The main purpose of this study was to create an electronic system (eEmergency system) in order to support, improve, and help the procedure of handling emergency calls. The main components and the architecture of this system are outlined in this paper. This system is being successfully used for 25 months and has been a useful tool from the beginning of the pandemic period of Covid-19

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature’s applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning

Abstract Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics

D2.3 - OPTIMAI - State of the art survey

This report is in fulfilment of requirements for Deliverable D2.3 of OPTIMAI. The document reports the State-of-the-Art in related scientific fields and identifies relevant research initiatives. Information contained herein is the result of activities performed in Task 2.2 (State of the art analysis, existing and past research initiatives). The key activities performed in this task are summarized in the following list: - Short introduction to Industry 4.0 to support the relevance and necessity of artificial intelligence in modern industry. - Assessment of the state-of-the-art within existing results coming from related projects, to identify which ones are relevant to OPTIMAI. This assessment was performed in terms of functionality provided, innovation capacity, technology, license, status, etc. - Assessment of the state-of-the-art within relevant scientific domains, including Artificial Intelligence (AI) for Industry, Metrology, AI-enhanced Digital Twins, Internet of Things (IoT) sensors, Computer Vision and Augmented Reality. - For the sake of completeness, a survey on ethical aspects is also performed. This is kept short since it is subject to other Deliverables of OPTIMAI. The review methodology is described in detail, in terms of sources, search keys, criteria for selection/exclusion etc., so that this work is repeatable. 269 articles were finally considered for inclusion in this report. Upon review of all relevant works, findings are summarized and discussed. The use of artificial intelligence technologies in various industrial fields is explored and investigated; enlightening graphs are produced to visualize the distribution and popularity of each AI-tech, implying its suitability for different purposes