BBC 2013 - 6th Workshop on "Biomedical and Bioinformatics Challenges for Computer Science" (BBC 2013)

Emerging technologies in genomics, proteomics, interactomics and other life science areas are generating an increasing amount of complex data and information. The evolving data and information ecosystem includes large experimental “omics” data sets, natural language text from the scientific literature and the Web, and highly connected heterogeneous information networks, such as open linked data distributed on the Internet. Integrating and analysing this information in the context of modern life science research problems and biomedical applications poses a considerable challenge for bioinformatics and computational biology. Traditionally, bioinformatics has concentrated on methods and technologies facilitating the acquisition, storage, organization, archiving, analysis and visualization of biological and medical data. 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, 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);
- Data and information management and integration (e.g., databases, data warehousing, data fusion);
- Knowledge discovery/management (e.g., knowledge bases, data mining, ontologies, workflow);
- Computational simulation (mechanistic, stochastic, multi-model);
- Artificial and computational intelligence (machine learning, agents, evolutionary techniques).
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) Data/information management and integration; (3) 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.
http://staff.icar.cnr.it/cannataro/bbc2013/