Nautilus is a novel challenge generated by the combination of two research areas: Systems Medicine and Big Data in Health. The group formed in Pisa, Italy, in 2012. This is a federated project with a global manifest.
This initiative follows the one embodied by LISM – Laboratory of Integrative Systems Medicine, and continues the backbone of the activities which were undertaken from 02/2012 till 10/2015 with LISM at IFC-CNR.
Nautilus’s aim is to conduct innovative and integrative research in biomedicine by leveraging the spectrum of Big Data applications in Systems Medicine, and with a focus on electronic-Health, mobile-Health, connected-Health, digital biomarkers, network imaging, clinical decision support systems.
- NRF Research Grant Competition 2013 | Title: Delta Networks: Study of Biological Networks under Differential Conditions: Applications to Cancer (Joint with UAEU, and run with LISM-IFC within CNR and Un Miami, US)
- Grant Competition in Oncology, by Istituto Toscano Tumori. | Title: Improving Therapy for Breast Cancer and Melanoma by Transcriptome-Methylome Profiling, Integrative Network Inference, and Design of Novel Theranostic Tools. (Participants are IFC-CNR Siena and IFAC-CNR Florence.)
- South of Italy project (coming soon . . .)
- Externautics (Siena) with R. Grifantini
- Computer Lab, Cambridge University (UK) with P. Lio’
- Center for Computational Science, University of Miami (Miami, FL)
- Sylvester Comprehensive Cancer Center, University of Miami (Miami, FL)
- Bioinformatics Lab, College of Information Technology, at the United Arab Emirates University (UAEU) with Nazar Zaki
- Beijing Genomics Institute (BGI) (Shenzen – China, and Europe HQ in DK)
- Biomaterials Science Center, University of Basel (CH)
What Systems Medicine is About:
Methodological, Applied, Theoretical: We apply Quantitative Modeling and Computational Tools with relevance and impacts measurable at systems scale. Due to the presence of large volumes of data, system’s complexity requires novel computational developments exploiting tools such as Networks, Machine Learning, Information Science, Bioinformatics and Statistical approaches to propose solutions.
Big Data is currently among the most valuable source of information and knowledge given its multiple potential uses, markets and increasing volume.
While it is clear that such value remains underestimated in the Health sector, and widespread access to Big Data is still limited, analytical and computational developments are destined to shape the field and determine its profitability at all levels.
In the cancer context, digital or e-health focus is on the identification of special populations of cancer patients with clinical and/or molecular characteristics which Big Data approaches will naturally tend to compare, aggregate, and analyze.
In clinical trials, about 3% of patients are on average gathered and such percentage is not only small but also marginally representative of patient populations. Big Data and the limitless knowledge discovery database set available, represents a novel frontier of investigation with impacts on diagnosis, therapy and prevention.
Electronic Health Records (EHR) provide (a) clinical decision support to doctors for selection of therapies, (b) valuable care assessment tools, (c) analytics to uncover hidden patterns in patients features including treatments, outcomes, etc.
Ideally, translational research considers science results and evidences first transmitted to clinical practice (bench to bedside), and then to public health (bedside to community).
Many factors influence public health: climate, culture, environment, and going deeper, temperature, education level, air quality, and also income, lifestyle, marital status, age, gender, religion, ethnic origins, till social relations mediated or not by social media.
EHR include data and images with static (census) and dynamic (panel) structures, in addition to other emerging sources like social media. This sort of ‘health data liquidity’ needs to be characterized by proper linkages for enabling interoperability and comparisons across various contexts.
Consequently, the concept that an ‘e-Health system’ is pursued as the ideal arrival point of an information process connecting Big Data components. This leads to the idea of c-Health or connected-Health.
Notably, the growth of probability of error is proportional to the system’s complexity, despite the number of individuals in the system can balance and allow inference to be carried out. This said, in the next future quantitative and computational sciences will be central resources and assets for analysis and inference.