Grid computing allows to couple geographically distributed resources and offers consistent and inexpensive access to resources irrespective of their physical location or access point. It enables sharing, selection, and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, computing clusters, storage systems, data sources and instruments, etc.), thus allowing them to be used as a single, unified resource for solving large-scale computing and data-intensive computing applications. Many biological and biomedical problems, such as molecular modeling for drug design, genetic &biochemical network, protein-protein interactions, phylogeny reconstruction, genetic linkage analysis, protein structure prediction, etc., require computationally intensive numerical operations on a large and, in many cases, distributed data domain. Similar other applications in biomedical research relying on efficient algorithms and the development of databases that manage complex biological information generated by genome projects can use the power of the grid. The BioGrid workshop focuses on the design, analysis, and implementation of high-level, architecture independent processing, storage, and retrieval technologies in the Grid environments for potential biological and biomedical applications. We also encourage papers reporting on original research (both theoretical and experimental) in parallel and distributed biomedical computations in the grid environments. Research outputs reporting results from broader biology-related domains, such as clinical practice, pharmaceutics, medical data storage and processing, and computerized epidemiology, etc., are to be solicited also.
| Wong, Elisabeth; Baur, Brittany; Quader, Saad et al. (2012) Biological network motif detection: principles and practice. Brief Bioinform 13:202-15 |
| Wong, Carol; Li, Yuran; Lee, Chih et al. (2011) Ensemble learning algorithms for classification of mtDNA into haplogroups. Brief Bioinform 12:1-9 |
| Lee, Chih; Nkounkou, Brittany; Huang, Chun-Hsi (2011) Comparison of LDA and SPRT on Clinical Dataset Classifications. Biomed Inform Insights 4:1-7 |
