Abstract. Annotation graph datasets are a natural representation of scientific knowledge. They are common in the life sciences where concepts such as genes and proteins are annotated with controlled vocabulary terms from ontologies. Scientists are interested in analyzing or mining these annotations, in synergy with the literature, to discover patterns. Further, annotated datasets provide an avenue for scientists to explore shared annotations across genomes to support cross genome discovery. We present a tool, PAnG (Patterns in Annotation Graphs), that is based on a complementary methodology of graph summarization and dense subgraphs. The elements of a graph summary correspond to a pattern and its visualization can provide an explanation of the underlying knowledge. We present and analyze two distance metrics to identify related concepts in ontologies. We present preliminary results using groups of Arabidopsis and C. elegans genes to illustrate the potential benefits of cross genome pattern discovery.
Linked Open Data has made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms (CV terms) from ontologies. These semantic annotations encode scientific knowledge which is captured in annotation datasets. One can mine these datasets to discover relationships and patterns between entities. Determining the relatedness (or similarity) between entities becomes a building block for graph pattern mining, e.g., identifying drug-drug relationships could depend on the similarity of the diseases (conditions) that are associated with each drug. Diverse similarity metrics have been proposed in the literature, e.g., i) stringsimilarity metrics; ii) path-similarity metrics; iii) topologicalsimilarity metrics; all measure relatedness in a given taxonomy or ontology. In this paper, we consider a novel annotation similarity metric AnnSim that measures the relatedness between two entities in terms of the similarity of their annotations. We model AnnSim as a 1-to-1 maximal weighted bipartite match, and we exploit properties of existing solvers to provide an efficient solution. We empirically study the effectiveness of AnnSim on real-world datasets of genes and their GO annotations, clinical trials, and a human disease benchmark. Our results suggest that AnnSim can provide a deeper understanding of the relatedness of concepts and can provide an explanation of potential novel patterns.
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