In the cases when public health authorities confirm patients with highly contagious diseases, they release summaries about patient locations and travel information to alert the public. Due to privacy concerns, these releases do not include the detailed data, and typically comprise the information about commercial facilities and public transportation used by the patients. This paper proposes a framework to represent the information in Resource Description Framework (RDF). The open data would then be used by residents for automated estimation of the potential risks of contacts, combined with the location information stored in their mobile phones.

For clinical decision support systems, the disease knowledge base accounts for the majority of the total development cost, because it requires considerable effort by highly-paid domain experts. However, existing research on automated acquisition of medical knowledge has focused on accuracy, while mostly ignoring the cost issue. This case study investigates the cost breakdown of our simplified disease knowledge base, and discusses a way of reducing development cost of the core component for clinical decision support systems. To achieve broad disease coverage with limited cost, we adopted a hybridization approach, that is, combining a handmade knowledge base for essential diseases and an automatically generated knowledge base for rare diseases. Toward further cost reduction, the case study suggested to minimize the human intervention by medical experts through i) keeping the structure of the knowledge base simple, ii) establishing a public resource for laboratory examination results, and iii) lazy evaluation of data quality in the utilization phase. Although the resulting knowledge base may not be adequate for a definitive diagnosis, the approach could be suitable to build clinical decision support systems for differential diagnosis and for disease search engines.

MOTIVATION: In early stage drug development, it is desirable to assess the toxicity of compounds as quickly as possible. Biomarker genes can help predict whether a candidate drug will adversely affect a given individual, but they are often difficult to discover. In addition, the mechanism of toxicity of many drugs and common compounds is not yet well understood. The Japanese Toxicogenomics Project provides a large database of systematically collected microarray samples from rats (liver, kidney and primary hepatocytes) and human cells (primary hepatocytes) after exposure to 170 different compounds in different dosages and at different time intervals. However, until now, no intuitive user interface has been publically available, making it time consuming and difficult for individual researchers to explore the data. RESULTS: We present Toxygates, a user-friendly integrated analysis platform for this database. Toxygates combines a large microarray dataset with the ability to fetch semantic linked data, such as pathways, compound-protein interactions and orthologs, on demand. It can also perform pattern-based compound ranking with respect to the expression values of a set of relevant candidate genes. By using Toxygates, users can freely interrogate the transcriptome's response to particular compounds and conditions, which enables deep exploration of toxicity mechanisms.