Discovering gene association networks by multi-objective evolutionary quantitative association rules
作者：
Highlights：
• An evolutionary multi-objective approach named GarNet is proposed to discover QAR.
• GarNet is used for inferring gene association networks from gene expression profiles.
• Garnet outperforms several benchmark methods in terms of precision and accuracy.
• The biological relevance has been analyzed using a GO-based enrichment analysis.
• The results from cell-cycle yeast data are consistent with the biological knowledge.
摘要
•An evolutionary multi-objective approach named GarNet is proposed to discover QAR.•GarNet is used for inferring gene association networks from gene expression profiles.•Garnet outperforms several benchmark methods in terms of precision and accuracy.•The biological relevance has been analyzed using a GO-based enrichment analysis.•The results from cell-cycle yeast data are consistent with the biological knowledge.
论文关键词：Data mining,Multi-objective evolutionary algorithms,Quantitative association rules,Gene networks,Microarray analysis
论文评审过程：Received 16 July 2012, Revised 26 November 2012, Accepted 14 March 2013, Available online 21 March 2013.
论文官网地址：https://doi.org/10.1016/j.jcss.2013.03.010
