Improving recommendation performance through ontology-based semantic similarity
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Ontology-Based genes similarity calculation with TF-IDF
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
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In this paper, we propose a novel method to measure the semantic similarity between genes. The key principle of our method relies on both path length between genes’ annotation terms in the Gene Ontology and depth of their annotation terms’ common ancestor node in the Gene Ontology. Our method applies an exponential transfer function which includes path length and depth as its two parameters to get the similarity of two annotation terms. We compute the arithmetic mean to get the similarity of genes. This measure ensures that the semantic similarity decreases with distance and increases with depth. A performance study with a set of genes from Saccharomyces Genome Database (SGD) has demonstrated that our method outperforms the previous leading measures in certain cases. We also analyzed several pathways from SGD and the clustering results showed that our method is quite competitive.