Due to the availability of high-throughput platforms (e.g. next generation sequencing, microarray and mass spectrometry) and clinical diagnostic tools (e.g. medical imaging), a recent trend in Bioinformatics and Biomedicine is the increasing production of experimental and clinical data.

Considering the complex analysis pipeline of the biomedical research, the bottleneck is more and more moving toward the storage, integration, and analysis of experimental data, as well as their correlation and integration with publicly available data banks.

While Parallel Computing and Grid Computing may offer the computational power and the storage to face this overwhelming availability of data, Cloud Computing is a key technology to hide the complexity of computing infrastructures, to reduce the cost of the data analysis task, and especially to change the overall model of biomedical research and health provision.

Grid infrastructures may offer the data storage needed to store the huge experimental and biomedical data, while parallel computing can be used for basic pre-processing (e.g. parallel BLAST, mpiBLAST) and for more advanced analysis (e.g. parallel data mining). In such a scenario, novel parallel architectures (e.g. CELL processors, GPUs, FPGA, hybrid CPU/FPGA) coupled with emerging programming models may overcome the limits posed by conventional computers to the mining and exploration of large amounts of data.

On the other hand, these technologies yet require great investments by biomedical and clinical institutions and are based on a traditional model where users often need to be aware and face different management problems, such as hardware and software management, data storage, software ownership, and not scalable costs (different professional-level applications in the biomedical domain have high starting costs that prevent many small laboratories to use them).

The Cloud Computing technology, that is able to offer scalable costs and increased reachability, availability and easiness of application use, and also the possibility to enforce collaboration among scientists, is already changing the business model in different domains and now it starts to be used also in the bioinformatics (see for instance the recent JCVI Cloud Bio-Linux initiative) and biomedical domains. However, many problems remain to be solved, such as availability and safety of the data, privacy-related issues, availability of software platforms for rapid deployment, execution and billing of biomedical applications.

The goal of ParBio is to bring together scientists in the fields of high performance and cloud computing, computational biology and medicine, to discuss, among the others, the organization of large scale biological and biomedical databases, the parallel/service-based implementation of bioinformatics and biomedical applications, and problems and opportunities of moving biomedical and health applications on the cloud.

The workshop will focus on research issues, problems and opportunities of moving biomedical and health applications on the cloud, as well as on the opportunity to define guidelines and minimum requirements for a Biomedical Cloud. Moreover, the workshop will discuss about parallel and distributed management and analysis of molecular and clinical data, that more and more need to be integrated and analysed in a joint way.


TOPICS OF INTEREST

The main themes and research topics will regard the applications of parallel and high performance computing to biology and medicine, as well as Cloud Computing opportunities and problems for bioinformatics and biomedical applications

- Large scale biological and biomedical databases

- Data integration and ontologies in biology and medicine

- Integration and analysis of molecular and clinical data

- Parallel bioinformatics algorithms

- Parallel visualization and exploration of omics and clinical data

- Parallel visualization and analysis of biomedical images

- Computing environments for large scale collaboration

- Scientific workflows in bioinformatics and biomedicine

- Emerging architectures and programming models for bioinformatics and biomedicine

- Parallel processing of bio-signals

- Modeling and simulation of complex biological processes

- Cloud Computing for bioinformatics and biomedicine

- Cloud Computing for health systems

- Privacy issues for Cloud-based biomedical applications

- (Web) Services for bioinformatics and biomedicine

- Grid Computing for bioinformatics and biomedicine

- Peer-To-Peer Computing for bioinformatics and biomedicine