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

机译:将语义相似性度量纳入遗传算法:一种用于搜索基因本体项的方法 ud

摘要

The most important property of the Gene Ontology isudthe terms. These control vocabularies are defined to provideudconsistent 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 similarityudmeasure 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.ud
机译:基因本体的最重要属性是术语。定义这些控制词汇以提供人类,软件代理或其他机器可读元数据可共享和可计算访问的基因产物的描述不一致的描述。每个术语都与诸如定义,同义词,数据库参考,氨基酸序列以及与其他术语的关系等信息相关联。这些信息使基因本体论广泛应用于微阵列和蛋白质组学分析中。但是,仍然使用基于关键字匹配的传统方法来执行搜索词的过程。这种方法的缺点是:忽略术语之间的语义关系,并且高度依赖专家来查找相似的术语。因此,本研究将语义相似性度量和遗传算法相结合,以执行更好的检索过程,以搜索语义相似的术语。使用语义相似度 udmeasure来计算两个词之间的相似度强度。然后,使用遗传算法执行批量检索并处理基因本体图的较大搜索空间的情况。计算结果表明该算法是有效的。 ud

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