8~(th) Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2015

摘要

1 Introduction Life science has been revolutionized by the new developed technologies which now produce an in-creased amount of complex data, especially influencing fields such as genomics, transcriptomics, and metagenomics. In fact, although the aims of bioinformatics were mainly devoted to the sup-port for the analysis of biological and medical data, like the acquisition, storage, organization, and archiving of data, the recent changes in the nature of the information produced by these new emerging technologies opened new challenges. More precisely, the new needs have made the role of computer science (both theoretical and applied aspects) much more central and critical in all the bioinformatics research directions, requiring the development of new methods and approaches to analyze the new produced data. On the other hand, computational biology was focused on modelling and simulating biomedical processes and systems, with particular atten-tion to the mathematical and computational aspects. Influenced by the advent of the developed technologies, these two disciplines are becoming more and more related each other. 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 ap-proaches are likely to involve advanced and large-scale computing techniques, computational approaches, technologies and infrastructures such as: (i) high-performance architectures and systems (e.g., multicore, GPU); (ii) distributed computing (e.g. grid, cloud, peer-to-peer); (iii) computational simulation (mechanistic, stochastic, multi-model); (iv) algorithms (theoretical and experimental aspects); (v) applied bioinformatics (analysis pipelines, tools, applications); (vi) artificial and computational intelligence (machine learning, agents, evolutionary techniques, bio-inspired methods).