POPTric: Pathway-based Order Preserving Triclustering for gene sample time data analysis

作者：

Highlights：

• A semi-supervised triclustering algorithm which is guided by KEGG pathway is presented here.

• To evaluate the effectiveness of the proposed algorithm synthetic dataset is used.

• The case study used breast cancer and HIV three dimensional gene expression data.

• The proposed algorithm shows better performance than state-of-the-art methods.

摘要

•A semi-supervised triclustering algorithm which is guided by KEGG pathway is presented here.•To evaluate the effectiveness of the proposed algorithm synthetic dataset is used.•The case study used breast cancer and HIV three dimensional gene expression data.•The proposed algorithm shows better performance than state-of-the-art methods.

论文关键词：Semi-supervised triclustering,Gene sample time data,Breast cancer data,Enrichment analysis,Hub gene identification,Pathway

论文评审过程：Received 12 February 2021, Revised 20 September 2021, Accepted 27 November 2021, Available online 20 December 2021, Version of Record 10 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116336