SICAGO: Semi-supervised cluster analysis using semantic distance between gene pairs in Gene Ontology | Zendy

BoYeong Kang | Zendy; Song Ko | Zendy; DaeWon Kim | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

SICAGO: Semi-supervised cluster analysis using semantic distance between gene pairs in Gene Ontology

Author(s) -

BoYeong Kang,

Song Ko,

DaeWon Kim

Publication year - 2010

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btq133

Subject(s) - gene ontology , cluster analysis , computer science , ontology , cluster (spacecraft) , semantic similarity , data mining , software , artificial intelligence , gene , natural language processing , information retrieval , biology , gene expression , genetics , philosophy , epistemology , programming language

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research