BILIS 2014 - International Workshop on High Performance Computing Systems for Bioinformatics and Life Sciences (BILIS 2014)

Incorporating new advancements of Information Technology (IT) in general and High Performance Computing (HPC) in particular in the domain of Life Sciences and Biomedical Research continues to receive tremendous attention of researchers, biomedical institutions and the rest of the biomedical community. Although medical instruments have benefited a great deal from the technological advances of the couple of decades, the impact of integrating IT advancements in addressing critical problems in biomedical research remains limited and the process of penetrating IT tools in the medical profession continues to be a very challenging problem. For example, the use of electronic medical records and Hospital Information Systems in improving health care remains fragmented. Similarly, the use of advanced computational tools seamlessly in the biomedical research cycle continues to be minimal.
Due to the computational intensive problems in life sciences, the marriage between the Bioinformatics domain and high performance computing is critical to the advancement of Biosciences. In addition, the problems in this domain tend to be highly parallelizable and deal with large datasets, hence using HPC is a natural fit. The Bioinformatics domain is rich in applications that require extracting useful information from very large and continuously growing sequence of databases. Most methods used for analyzing DNA/Protein sequences are known to be computationally intensive, providing motivation for the use of powerful computational systems with high throughput characteristics.
Moreover, high-throughput wet lab platforms such as next generation sequencing, microarray and mass spectrometry, are producing a huge amount of experimental "omics" data. The increasing availability of omics data poses new challenges to bioinformatics applications that need to face in a semi-automatic way an overwhelming availability of raw data. Main challenges regard: 1) the efficient storage, retrieval and integration of experimental data; 2) their efficient and high-throughput preprocessing and analysis; 3) the building of reproducible "in silico" experiments; 4) the integration of analysis results with pre-existing knowledge usually stored into ontologies.
As the storage, preprocessing and analysis of raw experimental data is becoming the main bottleneck of the analysis pipeline, parallel computing is playing an important role in all steps of the life sciences research pipeline, from raw data management and processing, to data integration and analysis, and to data exploration and visualization. Moreover, Cloud Computing is becoming the key technology to hide the complexity of computing infrastructures, to reduce the cost of the data analysis task, and especially to change the overall business model of biomedical research and health provision.
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 In such a scenario, large-scale distributed databases and parallel bioinformatics tools are key tools for organizing and exploring biological and biomedical data with the aim to discover new knowledge in biology and medicine.
In the current Information age, further progress of Medical Sciences requires successful integration with Computational and Information Sciences. The workshop attempts to attract innovative ways of how such integration can be achieved via Bioinformatics and Biomedical Informatics research, particularly in taking advantage of the new advancements in HPC systems. The focus of data analysis and data mining tools in biomedical research highlights the current state of research in the key biomedical research areas such as bioinformatics, medical informatics and biomedical imaging. Addressing performance concerns in managing and accessing medical data, while facilitating the ability to integrate and correlate different biomedical databases remains an outstanding problem in biomedical research. The amount of available biomedical data continues to grow in an exponential rate; however, the impact of utilizing such resources remains minimal. The development of innovative tools in HPC environments to integrate, analyze and mine such data sources is a key step towards achieving large impact levels.
The workshop focuses on topics related to the utilization of HPC systems and new models of parallel computing and cloud computing in problems related to Biomedical Informatics, along with the use of data integration and data mining tools to support biomedical research and Health Care.
The BILIS Workshop topics include (but are not limited to) the following:
HPC for the Analysis of Biological Data
Bioinformatics Tools for Health Care
Parallel Algorithms for Bioinformatics Applications
Ontologies in Biology and Medicine
Integration and Analysis of Molecular and Clinical Data
Parallel Bioinformatics Algorithms
Algorithms and Tools for Biomedical Imaging and Medical Signal Processing
Energy Aware Scheduling Techniques for Large Scale Biomedical Applications
HPC for analyzing Biological Networks
Next Generation Sequencing and Advanced Tools for DNA Assembly
HPC for Gene, Protein/RNA Analysis and Structure Prediction
Identification of Biomarkers
Biomedical Visualization Tools
Efficient Clustering and Classification Algorithms
Correlation Networks in Biomedical Research
Data Mining Techniques in Biomedical Applications
Heterogeneous Data Integration
HPC systems for Ontology and Database Integration
Pattern Recognition and Search Tools in Biological and Clinical Databases
Ubiquitous Medical Knowledge Discovery and Exchange
HPC for Monitoring and Treatment Facilities
Drug Design and Modeling
Computer Assisted Surgery and Medical Procedures
Remote Patient Monitoring, Homecare Applications
Mobile and Wireless Healthcare and Biomedical Applications
Cloud Computing for Bioinformatics, Medicine, and Health Systems