The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.

BACKGROUND: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery. RESULTS: We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action. CONCLUSIONS: This work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine. AVAILABILITY: TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.

Linked open drug data for pharmaceutical research and development.

There is an abundance of information about drugs available on the Web. Data sources range from medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in clinical trials. These data are typically not connected together, which reduces the ease with which insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web Consortium's (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly available data about drugs, created Linked Data representations of the data sets, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task force provides recommendations for the best practices of exposing data in a Linked Data representation. In this paper, we present past and ongoing work of LODD and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing.

Environmental stress and fluctuating asymmetry in the grasshopper Chorthippus parallelus (Acrididae: Gomphocerinae).

We examined the effect of agrochemical pollution/fertilisation of the soil and climatic conditions at high altitudes on fluctuating asymmetry (FA) level in hindfemur and forewing lengths of the grasshopper Chorthippus parallelus (Zetterstedt). All traits and samples exhibited ideal FA. Forewings, which are less functionally significant in this species, generally exhibited greater FA than the more functionally significant femora. Forewing FA was also more affected by stress induced by climatic conditions at high altitudes than femur FA. The high altitude sample was the most asymmetric sample for both traits, followed by the two control samples. The two samples from agrochemically contaminated habitats showed the lowest asymmetry in femora and forewings of C. parallelus. These results suggest that high altitude imposes more stress on this grasshopper species than does agrochemical contamination although the stress response was more dramatic in the less functional trait.