Semantics-enabled service discovery framework in the SIMDAT pharma grid.

We present the design and implementation of a semantics-enabled service discovery framework in the data Grids for process and product development using numerical simulation and knowledge discovery (SIMDAT) Pharma Grid, an industry-oriented Grid environment for integrating thousands of Grid-enabled biological data services and analysis services. The framework consists of three major components: the Web ontology language (OWL)-description logic (DL)-based biological domain ontology, OWL Web service ontology (OWL-S)-based service annotation, and semantic matchmaker based on the ontology reasoning. Built upon the framework, workflow technologies are extensively exploited in the SIMDAT to assist biologists in (semi)automatically performing in silico experiments. We present a typical usage scenario through the case study of a biological workflow: IXodus.

A service-oriented grid infrastructure for biomedical data and compute services.

Service-oriented Grid technologies are increasingly utilized for the realization of future biomedical IT infrastructures since they offer unprecedented opportunities for the integration of advanced analysis and simulation applications as well as distributed heterogeneous data sources and information systems. The European Union's @neurIST project is developing a Grid-based IT infrastructure for the management of all processes linked to research, diagnosis, and treatment development for complex and multifactorial diseases encompassing data repositories, computational analysis services, and information systems handling multiscale, multimodal information at distributed sites. This paper provides an overview of the @neurIST Grid middleware and outlines the infrastructure offered for the provision of advanced compute and data services to support computationally demanding modeling and simulation tasks and to access heterogeneous distributed data sources through semantic integration.

The @neurIST ontology of intracranial aneurysms: providing terminological services for an integrated IT infrastructure.

The @neurIST ontology is currently under development within the scope of the European project @neurIST intended to serve as a module in a complex architecture aiming at providing a better understanding and management of intracranial aneurysms and subarachnoid hemorrhages. Due to the integrative structure of the project the ontology needs to represent entities from various disciplines on a large spatial and temporal scale. Initial term acquisition was performed by exploiting a database scaffold, literature analysis and communications with domain experts. The ontology design is based on the DOLCE upper ontology and other existing domain ontologies were linked or partly included whenever appropriate (e.g., the FMA for anatomical entities and the UMLS for definitions and lexical information). About 2300 predominantly medical entities were represented but also a multitude of biomolecular, epidemiological, and hemodynamic entities. The usage of the ontology in the project comprises terminological control, text mining, annotation, and data mediation.

Information extraction in the life sciences: perspectives for medicinal chemistry, pharmacology and toxicology.

Information extraction approaches have been successfully applied to mine the scientific literature in biology and medicine. So far, the main focus of research and development in this domain was on the recognition and extraction of gene and protein names in the context of molecular biology and genome research and on disease names and other medical terms in the context of clinical research. Similar to biology and medical sciences, medicinal chemistry, pharmacology and toxicology are descriptive sciences. However, information extraction approaches in these disciplines encounter a number of problems that are specific to the fact that these scientific areas are essentially centred at chemical compounds and their structures. In this review, we will give a short overview on general information extraction strategies in the life sciences and we will introduce new approaches to apply information extraction to the domain of pharmacology, medicinal chemistry and toxicology. Finally, we will emphasize on how information extraction approaches will support public and commercial research in medicinal chemistry, pharmacology and toxicology by linking information on chemical structures to biological information.

Sequence and analysis of chromosome 2 of Dictyostelium discoideum.

The genome of the lower eukaryote Dictyostelium discoideum comprises six chromosomes. Here we report the sequence of the largest, chromosome 2, which at 8 megabases (Mb) represents about 25% of the genome. Despite an A + T content of nearly 80%, the chromosome codes for 2,799 predicted protein coding genes and 73 transfer RNA genes. This gene density, about 1 gene per 2.6 kilobases (kb), is surpassed only by Saccharomyces cerevisiae (one per 2 kb) and is similar to that of Schizosaccharomyces pombe (one per 2.5 kb). If we assume that the other chromosomes have a similar gene density, we can expect around 11,000 genes in the D. discoideum genome. A significant number of the genes show higher similarities to genes of vertebrates than to those of other fully sequenced eukaryotes. This analysis strengthens the view that the evolutionary position of D. discoideum is located before the branching of metazoa and fungi but after the divergence of the plant kingdom, placing it close to the base of metazoan evolution.