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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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Correlation between Gene Expression and GO Semantic Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Fuzzy Measures on the Gene Ontology for Gene Product Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
Analysis of crossovers and selections in a coarse-grained parallel genetic algorithm
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A Hybrid Concept Similarity Measure Model for Ontology Environment
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
Journal of Network and Computer Applications
Ontology-based resources for bioinformatics analysis
International Journal of Metadata, Semantics and Ontologies
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A genetic similarity algorithm is introduced in this study to find a group of semantically similar Gene Ontology terms. The genetic similarity algorithm combines semantic similarity measure algorithm with parallel genetic algorithm. The semantic similarity measure algorithm is used to compute the similitude strength between the Gene Ontology terms. Then, the parallel genetic algorithm is employed to perform batch retrieval and to accelerate the search in large search space of the Gene Ontology graph. The genetic similarity algorithm is implemented in the Gene Ontology browser named basic UTMGO to overcome the weaknesses of the existing Gene Ontology browsers which use a conventional approach based on keyword matching. To show the applicability of the basic UTMGO, we extend its structure to develop a Gene Ontology -based protein sequence annotation tool named extended UTMGO. The objective of developing the extended UTMGO is to provide a simple and practical tool that is capable of producing better results and requires a reasonable amount of running time with low computing cost specifically for offline usage. The computational results and comparison with other related tools are presented to show the effectiveness of the proposed algorithm and tools.