Network medicine for patients' stratification: From single-layer to multi-omics

Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings. This article is categorized under: Cancer > Biomedical Engineering Cancer > Computational Models Cancer > Genetics/Genomics/Epigenetics.

Workflow for health-related and brain data lifecycle

Poor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to change the current lifestyle. In conjunction with data representing human brain activity, we can obtain and identify human health problems resulting from a long-term lifestyle more precisely and, where appropriate, improve the quality and length of human life. Currently, brain and physical health-related data are not commonly collected and evaluated together. However, doing that is supposed to be an interesting and viable concept, especially when followed by a more detailed definition and description of their whole processing lifecycle. Moreover, when best practices are used to store, annotate, analyze, and evaluate such data collections, the necessary infrastructure development and more intense cooperation among scientific teams and laboratories are facilitated. This approach also improves the reproducibility of experimental work. As a result, large collections of physical and brain health-related data could provide a robust basis for better interpretation of a person's overall health. This work aims to overview and reflect some best practices used within global communities to ensure the reproducibility of experiments, collected datasets and related workflows. These best practices concern, e.g., data lifecycle models, FAIR principles, and definitions and implementations of terminologies and ontologies. Then, an example of how an automated workflow system could be created to support the collection, annotation, storage, analysis, and publication of findings is shown. The Body in Numbers pilot system, also utilizing software engineering best practices, was developed to implement the concept of such an automated workflow system. It is unique just due to the combination of the processing and evaluation of physical and brain (electrophysiological) data. Its implementation is explored in greater detail, and opportunities to use the gained findings and results throughout various application domains are discussed.

The Increasing Population Movements in the 21st Century: A Call for the E-Register of Health-Related Data Integrating Health Care Systems in Europe

The escalating mass influx of people to Europe in the 21st century due to geopolitical and economic reasons as well as food crises ignites significant challenges for national health care services. The lack or disruption of cross-border, e-transferred, health-related data negatively affects the health outcome and continuous care, particularly in medically compromised individuals with an unsettled status. Proposal: The urgent need of a structured database, in the form of a health-related data register funded by the European Union that allows a swift exchange of crucial medical data, was discussed to flag ever-increasing migrants' health problems, with a primary aim to support an adequate health care provision for underserved people who are at risk of deteriorating health. The data security information technology aspects, with a proposed and drafted structure of an e-health register, were succinctly highlighted. Conclusions: Focusing on long-term benefits and considering future waves of mass relocation, an investment in a health-related data register in Europe could vastly reduce health care disparities between minority groups and improve epidemiological situations with regard to major illnesses, including common, communicable diseases as well as oncological and infectious conditions. Commissioners, policymakers, and stakeholders are urged to continue a collective action to ensure vulnerable people can access health services by responding to the ongoing global migration crisis.

SPHN - The BioMedIT Network: A Secure IT Platform for Research with Sensitive Human Data

The BioMedIT project is funded by the Swiss government as an integral part of the Swiss Personalized Health Network (SPHN), aiming to provide researchers with access to a secure, powerful and versatile IT infrastructure for doing data-driven research on sensitive biomedical data while ensuring data privacy protection. The BioMedIT network gives researchers the ability to securely transfer, store, manage and process sensitive research data. The underlying BioMedIT nodes provide compute and storage capacity that can be used locally or through a federated environment. The network operates under a common Information Security Policy using state-of-the-art security techniques. It utilizes cloud computing, virtualization, compute accelerators (GPUs), big data storage as well as federation technologies to lower computational boundaries for researchers and to guarantee that sensitive data can be processed in a secure and lawful way. Building on existing expertise and research infrastructure at the partnering Swiss institutions, the BioMedIT network establishes a competitive Swiss private-cloud - a secure national infrastructure resource that can be used by researchers of Swiss universities, hospitals and other research institutions.