PREDICTING SIGNIFICANCE OF UNKNOWN VARIANTS IN GLIAL TUMORS THROUGH SUB-CLASS ENRICHMENT.

Glial tumors have been heavily studied and sequenced, leading to scores of findings about altered genes. This explosion in knowledge has not been matched with clinical success, but efforts to understand the synergies between drivers of glial tumors may alleviate the situation. We present a novel molecular classification system that captures the combinatorial nature of relationships between alterations in these diseases.

Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships.

Background: Discovering that drug entities already approved for one disease are effective treatments for other distinct diseases can be highly beneficial and cost effective. To do this predictively, our conjecture is that a semantic infrastructure linking mechanistic relationships between pharmacologic entities and multidimensional knowledge of biological systems and disease processes will be highly enabling.

Results: To develop a knowledge framework capable of modeling and interconnecting drug actions and disease mechanisms across diverse biological systems contexts, we designed a Disease-Drug Correlation Ontology (DDCO), formalized in OWL, that integrates multiple ontologies, controlled vocabularies, and data schemas and interlinks these with diverse datasets extracted from pharmacological and biological domains.

Identifying disease-causal genes using Semantic Web-based representation of integrated genomic and phenomic knowledge.

Most common chronic diseases are caused by the interactions of multiple factors including the influences and responses of susceptibility and modifier genes that are themselves subject to etiologic events, interactions, and environmental factors. These entities, interactions, mechanisms, and phenotypic consequences can be richly represented using graph networks with semantically definable nodes and edges. To use this form of knowledge representation for inferring causal relationships, it is critical to leverage pertinent prior knowledge so as to facilitate ranking and probabilistic treatment of candidate etiologic factors.

Mining human phenome to investigate modularity of complex disorders.

A principal goal for biomedical research is to improve our understanding of factors that control clinical disease phenotypes. Among genetically-determined diseases, identical mutations may exhibit substantial phenotype variance by individual and background strain, suggesting both environmental and genetic mutant allele interactions. Moreover, different diseases can share phenotypic features extensively.

BioDash: a Semantic Web dashboard for drug development.

A researcher's current scientific understanding is assembled from multiple sources of facts and knowledge, along with beliefs and hypotheses of their interpretations. A comprehensive and structured aggregation of all the relevant components is to-date not possible using standard database technologies, nor is it obvious how to include beliefs, such as models and hypotheses into such a bundle. When such information is required as the basis for important decision-making (e.