A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes

• URL: http://arxiv.org/abs/2005.05519v1
• Date: Tue, 12 May 2020 02:04:40 GMT
• Title: A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes
• Authors: Kaijie Xu, Witold Pedrycz, Zhiwu Li, Yinghui Quan, and Weike Nie
• Abstract summary: Bi-clustering methods are used to mine bi-clusters whose subsets of samples (genes) are co-regulated under their test conditions. Unfortunately, traditional bi-clustering methods are not fully effective in discovering such bi-clusters. We propose a novel bi-clustering method by involving here the theory of Granular Computing.
• Score: 76.84066556597342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional clustering methods are limited when dealing with huge and heterogeneous groups of gene expression data, which motivates the development of bi-clustering methods. Bi-clustering methods are used to mine bi-clusters whose subsets of samples (genes) are co-regulated under their test conditions. Studies show that mining bi-clusters of consistent trends and trends with similar degrees of fluctuations from the gene expression data is essential in bioinformatics research. Unfortunately, traditional bi-clustering methods are not fully effective in discovering such bi-clusters. Therefore, we propose a novel bi-clustering method by involving here the theory of Granular Computing. In the proposed scheme, the gene data matrix, considered as a group of time series, is transformed into a series of ordered information granules. With the information granules we build a characteristic matrix of the gene data to capture the fluctuation trend of the expression value between consecutive conditions to mine the ideal bi-clusters. The experimental results are in agreement with the theoretical analysis, and show the excellent performance of the proposed method.

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