Informative top-k class associative rule for cancer biomarker discovery on microarray data
作者：
Highlights：
• Biomarker discovery on microarray data for colorectal cancer and breast cancer.
• Integration with gene ontology, KEGG pathways, and protein-protein interactions.
• Rule interestingness based on information gain, accuracy, and enrichment score.
• Information gain and associative classification produce higher prediction.
• Significant reproducibility and interpretability of discovered genes.
摘要
•Biomarker discovery on microarray data for colorectal cancer and breast cancer.•Integration with gene ontology, KEGG pathways, and protein-protein interactions.•Rule interestingness based on information gain, accuracy, and enrichment score.•Information gain and associative classification produce higher prediction.•Significant reproducibility and interpretability of discovered genes.
论文关键词：Microarray gene expression,Associative classification,Information gain,Biomarker discovery,Colorectal cancer,Breast cancer
论文评审过程：Received 22 September 2019, Revised 4 December 2019, Accepted 28 December 2019, Available online 30 December 2019, Version of Record 7 January 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113169
