1) Decoding immune profiles OR Immune profiling
Immunoglobulins (B cell receptors) and T cell receptors are crucial factors of immune response against pathogens. We examine various receptors characteristics regarding mainly diversity, reactivity, sequence and structural patterns using low and, recently developed, high throughput sequencing technologies in different scenarios ranging from leukemia to allergy.
2) Investigating the tumor-microenvironment crosstalk
The tumor microenvironment is the cellular environment in which the tumor exists, and includes immune cells, surrounding blood vessels, and other soluble factors. The tumor and the surrounding microenvironment are closely related and interact constantly through receptors which bind to different substances. We study the immune receptors activation, which contributes to tumor heterogeneity by triggering a biochemical chain of events inside the cell, eventually affecting tumor cell fate.
3) Cancer Genomics and Epigenomics
A genome is an organism’s complete set of DNA. Each genome encompasses all the information needed to build and maintain that organism. Genes, the basic unit of the genome, are often called the blueprint for life because they tell each of your cells what to do and when to do it.
Epigenomics literally means “on top of” genomics. It refers to external modifications to DNA that turn genes “on” or “off.” These modifications do not change the DNA sequence, but instead, they affect how cells “read” genes. If DNA is the “hardware”, epigenetics is the “software”.
It is well known that alterations at both these levels play a central role in cancer development. We study the genomic and epigenomic profiles of patients with lymphoid malignancies in order to dissect the underlying mechanisms.
4) Mechanisms of neurodegeneration
Neurodegenerative diseases are caused by misfolded proteins that lose their normal conformation and subsequently, their function. Despite the fact that several mutations are related to protein misfolding, in most cases the exact pathogenic mechanisms remain largely unknown. Here, we attempt to unravel these mechanisms using genetic or biochemical assays in disease cell models.
5) Handling systems for Clinicobiological data
6) Clinical epidemiology
7) Biostatistics & Bioinformatics
Big Data Analytics for Health
Health is an exemplar case of big data production. Our research in the domain focuses on developing methods for big biodata management, modelling and analytics, in domains such as the Intensive Care Unit, large administrative claims healthcare databases, drug safety data, Next Generation Sequencing data etc. Our aim is twofold: (a) effective management of the large and rapidly produced data volumes, and (b) to extract value out of these data based on their transformation and processing, in order to support healthcare professionals and researchers in evidence-informed decision making (e.g. to conduct hypothesis testing, extract new prognostic features, identify disease risk factors, etc.).
- AEGLE cases
- Systems for active drug surveillance
Layman message: How can we effectively manage and extract value from big data in health, in order to leverage diagnosis, prognosis, prevention and, overall, better management of diseases? show less
Design, Development and Evaluation of Systems/Services Exploiting New Types of Personal, Health-related Data (the Quantified Self principle)
Recent technological advances enable to unobtrusively and pervasively obtain insights on parameters related with health (e.g. biosignal measurements) and lifestyle (physical activity – exercise, nutrition, social interaction etc.). Our research in the domain is patient-centered and focuses on: (a) patient/citizen empowerment to self-manage their disease and well-being, and (b) independent living and support of the elderly and vulnerable populations. To address the above goals, we design, develop and evaluate appropriate interventions in the form of systems/services for health monitoring and lifestyle guidance towards a healthy, safe and active living. show more Examples: Layman message: How can we capture and exploit health and lifestyle related data which are explicitly/implicitly captured by new technological artifacts e.g. smartphones and apps, measuring devices, etc. to deliver personalised health and well-being services? show less
Layman message: How can we capture and exploit health and lifestyle related data which are explicitly/implicitly captured by new technological artifacts e.g. smartphones and apps, measuring devices, etc. to deliver personalised health and well-being services? show less
Semantic Integration of Heterogeneous Biomedical data – Linked Biodata
Many applications in the health domain concern exploiting data originated from multiple, heterogeneous sources to address their common analysis and linkage. Our research in the domain focuses on the use/development of appropriate terminologies, ontologies and semantic technologies, aiming to link data and create combinatorial knowledge sources, leveraging this way the potential to exploit these data for biomedical research (e.g. integrated safety profile of marketed drugs, support of multicentre association studies, etc.). show more Examples: Layman message: How can we better exploit data originated from multiple, heterogeneous sources for applications in health? show less
Layman message: How can we better exploit data originated from multiple, heterogeneous sources for applications in health? show less
Learning Health Systems
It has been widely argued that the secondary use of data that are routinely collected in the healthcare sector (Electroni Health Record, Administrative Claims Databases, etc.) could be exploited for developing the so-called Learning Health Systems. Such systems aspire to contribute in achieving continuous rapid improvement in health and healthcare by providing important insights concerning e.g. personalization of health, treatment efficacy, safety issues, etc. show more Examples: Layman message: How can we develop systems capable of learning from routinely collected data (secondary use) in the healthcare domain, in order to foster healthcare service delivery? show less
Layman message: How can we develop systems capable of learning from routinely collected data (secondary use) in the healthcare domain, in order to foster healthcare service delivery? show less