Details

Autor(en) / Beteiligte

• Kudo, Yasuo
• Okada, Yoshifumi
• Murai, Tetsuya
• Usui, Shiro
• Zhong, Ning
• Shirao, Tomoaki
• Kasamatsu, Takuji
• Imamura, Kazayuki
• Schwabe, Lars

Titel

On a Possibility of Applying Interrelationship Mining to Gene Expression Data Analysis

Ist Teil von

• Brain and Health Informatics, p.379-388

Ort / Verlag

Cham: Springer International Publishing

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Interrelationship mining was proposed by the authors to extract characteristics of objects based on interrelationships between attributes. Interrelationship mining is an extension of rough set-based data mining, which enables us to extract characteristics based on comparison of values of two different attributes such that “the value of attribute a is higher than the value of attribute b.” In this paper, we discuss an approach of applying the interrelationship mining to bioinformatics, in particular, gene expression data analysis.