With the increasing number of ontologies being designed to represent and manage knowledge in all sorts of sectors, ontology alignment and integration become more and more important in aggregating intelligent efforts on homogenous and heterogeneous data. From the computational perspective, it is challenging due to the ubiquitous existence of diverse classifications of same data. In this paper, we propose an active ontology integration and alignment system, which plugs in expandable learning reference context pool. In the reference context pool, we have integrated WordNet, MeSH, and external curated mapping sources (ICD9 to SNOMEDCT) with an extension to injecting UMLS. The active ontology integration and alignment system takes account of not only subsumption tree but also directed acyclic graph underlying ontologies. It allows 1) finding exact one-to-one matching terms of pairwise ontologies, 2) finding inexact one-to-one term mappings, where two terms have at least a concept in common on basis of the lexical context, and 3) finding one-to-many concept mappings, where one concept can be lexically mapped to the combination of multiple exclusive concepts.

MOTIVATION: The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. RESULTS: We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. AVAILABILITY AND IMPLEMENTATION: http://www.newdrugtargets.org. CONTACT: [email protected].

Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to capture and structure therapeutic intent for computational reuse, thus enabling more sophisticated decision-support tools and a possible mechanism for computer-aided drug repurposing. The indications for drugs, such as those expressed in the Structured Product Labels approved by the US Food and Drug Administration, appears to be a tractable area for developing an application ontology of therapeutic intent.

SUMMARY: The increasing availability of experimentally determined binding affinities for drugs on multiple protein targets requires the design of specific mining and visualization tools that graphically integrate chemical and biological data in an efficient environment. With this aim, we developed iPHACE, an integrative web-based tool to navigate in the pharmacological space defined by small molecule drugs contained in the IUPHAR-DB, with additional interactions present in PDSP. Extending beyond traditional querying and filtering tools, iPHACE offers a means to extract knowledge from the target profile of drugs as well as from the drug profile of protein targets. AVAILABILITY: iPHACE is available at http://cgl.imim.es/iphace/ (EU site) and http://agave.health.unm.edu/iphace/ (US mirror).