Incorporating semantic similarity measure in genetic algorithm: an approach for searching the gene ontology terms

• Main Authors: M. Othman, Razib, Deris, Safaai, M. Ilias, Rosli, Alashwal, Hany Taher Ahmed, Hassan, Rohayanti, Mohamed, Farhan
• Format: Article
• Language: English
• Published: World Academy of Science, Engineering and Technology 2007
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/8432/1/RMOthman2007-Incorporating_Semantic_Similarity_Measure_In.pdf; http://eprints.utm.my/id/eprint/8432/; http://www.waset.org/ijci/

The most important property of the Gene Ontology is the terms. These control vocabularies are defined to provide consistent descriptions of gene products that are shareable and computationally accessible by humans, software agent, or other machine-readable meta-data. Each term is associated with information such as definition, synonyms, database references, amino acid sequences, and relationships to other terms. This information has made the Gene Ontology broadly applied in microarray and proteomic analysis. However, the process of searching the terms is still carried out using traditional approach which is based on keyword matching. The weaknesses of this approach are: ignoring semantic relationships between terms, and highly depending on a specialist to find similar terms. Therefore, this study combines semantic similarity measure and genetic algorithm to perform a better retrieval process for searching semantically similar terms. The semantic similarity measure is used to compute similitude strength between two terms.Then, the genetic algorithm is employed to perform batch retrievals and to handle the situation of the large search space of the Gene Ontology graph. The computational results are presented to show the effectiveness of the proposed algorithm.