Emerging technologies in genomics, transcriptomics, metagenomics and other life science areas are generating an increasing amount of complex data and information. Traditionally, bioinformatics has been focused on the design of methods and technologies facilitating the acquisition, storage, organization, archiving, analysis and visualization of biological and medical data. However, recent changes related to the emerging technologies have made the role of computer science (both theoretical and applied aspects) much more critical in all the bioinformatics research directions.

Computational biology, on the other hand, has emphasized mathematical and computational techniques facilitating the modelling and simulation of biomedical processes and systems.

In recent years the distinction between these two fields has become increasingly blurred. In order to tackle the growing complexity associated with emerging and future life science challenges, bioinformatics and computational biology researchers and developers need to explore, develop and apply novel computational concepts, methods, tools and systems.

Many of these new approaches are likely to involve advanced and large-scale computing techniques, computational approaches, technologies and infrastructures such as:

  • High-performance architectures and systems (e.g. multicore, GPU);
  • Distributed computing (e.g. grid, cloud, peer-to-peer, Web services, e-infrastructures);
  • Computational simulation (mechanistic, stochastic, multi-model);
  • Algorithms (theoretical and experimental aspects);
  • Applied bioinformatics (analysis pipelines, tools, applications);
  • Artificial and computational intelligence (machine learning, agents, evolutionary techniques, bio-inspired methods).
  • Network-based analysis for epidemics
  • Data Science for Public Health decision making
  • Modelling and simulation of virus diffusion
  • Telemedicine Infrastructures and Sensors for collecting Public Health citizens data

Together, these topics cover the key bioinformatics and computational biology techniques and technologies encountered in modern life science environments:

  1. Advanced computing architectures/infrastructures
  2. Algorithm design
  3. Data/information management and integration Data/information analysis and knowledge discovery
  4. Integration of quantitative/symbolic knowledge into executable biomedical “theories” or models.

The aim of this workshop is to bring together computer and life scientists to discuss emerging and future directions in these areas.

Create your website with
Get started
%d bloggers like this: