School of Mechanical Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea.
Bioinformatics. 2010 May 15;26(10):1384-5. doi: 10.1093/bioinformatics/btq133. Epub 2010 Mar 26.
Despite the importance of using the semantic distance to improve the performance of conventional expression-based clustering, there are few freely available software that provides a clustering algorithm using the ontology-based semantic distances as prior knowledge. Here, we present the SICAGO (SemI-supervised Cluster Analysis using semantic distance between gene pairs in Gene Ontology) system that helps to discover the groups of genes more effectively using prior knowledge extracted from Gene Ontology.
http://ai.cau.ac.kr/sicago.html
尽管利用语义距离来提高传统基于表达的聚类算法的性能非常重要，但目前很少有免费的软件提供基于本体论的语义距离作为先验知识的聚类算法。在这里，我们介绍了 SICAGO（使用基因本体论中基因对之间的语义距离进行半监督聚类分析）系统，该系统有助于利用从基因本体论中提取的先验知识更有效地发现基因群。
http://ai.cau.ac.kr/sicago.html
