Mining high average-utility sequential rules to identify high-utility gene expression sequences in longitudinal human studies

作者：

Highlights：

• We have proposed HAUS-rules, a new MOEA for mining HAUSRs from sequence datasets.

• Utility and interestingness are maximized to obtain understandable HAUSRs.

• The rules extracted from temporal microarrays present a high biological relevance.

摘要

•We have proposed HAUS-rules, a new MOEA for mining HAUSRs from sequence datasets.•Utility and interestingness are maximized to obtain understandable HAUSRs.•The rules extracted from temporal microarrays present a high biological relevance.

论文关键词：High average-utility sequential rules,Multi-objective evolutionary algorithm,eXplainable artificial intelligence,Gene expression patterns,Obesity

论文评审过程：Received 1 March 2021, Revised 29 October 2021, Accepted 11 December 2021, Available online 6 January 2022, Version of Record 18 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116411