An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Greedy approximation algorithms for finding dense components in a graph
APPROX '00 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization
Finding a Maximum Density Subgraph
Finding a Maximum Density Subgraph
Taxonomy learning: factoring the structure of a taxonomy into a semantic classification decision
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A new method to measure the semantic similarity of GO terms
Bioinformatics
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Lowest common ancestors in trees and directed acyclic graphs
Journal of Algorithms
Link prediction for annotation graphs using graph summarization
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Dense subgraphs with restrictions and applications to gene annotation graphs
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
PAnG: finding patterns in annotation graphs
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Measuring Relatedness Between Scientific Entities in Annotation Datasets
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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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.