In the last few years we have witnessed an exponential growth of biological and medical data, coming from the always more advanced sequencing technologies, clinical and imaging data, electronic health records, etc.. The resulting data are complex in contents, heterogeneous in formats and order of Terabytes in amount. These “big data” in the biological and medical domain provide unprecedented opportunities to work on exciting problems, but also raise many new challenges for data storage, process and mining. A very challenging scenario is that of Personalized (or Precision) Medicine, according to which medical decisions, treatments, practices, or products should be tailored to the individual patient. In this respect, the selection of appropriate and optimal therapies can be based on the context of a patient’s genetic content or other molecular or cellular analysis. To this aim, heterogeneous data collected from different sources, such as genetic heritage, lifestyle and environmental context, may be combined in order to advance disease understanding, diagnosis and treatment, and ensure delivery of appropriate therapies.
The First International Workshop on BIG data storage, processing and mining for Personalized MEDicine will be held in conjunction with the 22nd European Conference on Advances in Databases and Information Systems ADBIS 2018. It will provide to participants the opportunity of introducing and discussing new methods, theoretical approaches, algorithms, tools, and platforms that are relevant for the database community in the domain of Personalized Medicine. This will hopefully imply also the definition of new problems raised in managing complex data, and the dissemination of novel ideas on the application of “big data” methodologies in the biological and medical domain.
The list of topics for the workshop, that has not to be intended as exhaustive, is reported below.
- Integration of biomedical databases and sources
- High Performance Computing architectures and/or applications for omics data
- Parallel Machine Learning approaches for personalized medicine
- Next-Generation Sequencing data analysis and interpretation
- NoSQL databases in healthcare
- Big data analytics for omics data
- Data privacy and security in healthcare
- Clinical decision support systems, Healthcare Systems
- Models for clinic and genetic data