Abstract

In the current paper, a novel approach is proposed for bi-clustering of gene expression data using the fusion of differential evolution framework and self-organizing map (SOM), named as BiClustSMEA. Variable number of gene and condition cluster centers are encoded in different solutions of the population to determine the number of bi-clusters from a dataset in an automated way. The concept of SOM is utilized in designing new genetic operators for both gene and condition clusters to reach to the optimal solution in a faster way. In order to measure the goodness of a bi-clustering solution, three bi-cluster quality measures, mean squared error, row variance, and bi-cluster size, are optimized simultaneously using differential evolution as the underlying optimization strategy. The concept of polynomial mutation is incorporated in our framework to generate highly diverse solutions which in turn helps in faster convergence. The proposed approach is applied on two real-life microarray gene expression datasets and results are compared with various state-of-the-art techniques. Results obtained clearly illustrate that our approach extracts high-quality bi-clusters as compared to other methods and also it converges much faster than other competitors. Further, the obtained results are validated using statistical significance test and biological significance test.

中文翻译：

---

使用自组织多目标进化算法从微阵列数据自动进化双簇

摘要

在当前的论文中，提出了一种新的方法，该方法利用差异进化框架和自组织图（SOM）的融合对基因表达数据进行双聚类，称为BiClustSMEA。在种群的不同解决方案中编码可变数量的基因和条件簇中心，以自动方式从数据集中确定双簇的数目。SOM的概念可用于为基因和条件簇设计新的遗传算子，从而以更快的方式达到最佳解决方案。为了衡量双群集解决方案的优劣，同时使用差分进化作为基础优化策略，同时优化了三个双群集质量度量，即均方误差，行方差和双群集大小。多项式突变的概念被并入我们的框架中，以生成高度多样化的解决方案，进而有助于更快地收敛。所提出的方法应用于两个现实生活中的微阵列基因表达数据集，并将结果与​​各种最新技术进行了比较。获得的结果清楚地表明，与其他方法相比，我们的方法可提取高质量的双簇，并且其收敛速度比其他竞争对手快得多。此外，使用统计显着性检验和生物学显着性检验来验证所获得的结果。获得的结果清楚地表明，与其他方法相比，我们的方法可提取高质量的双簇，并且收敛速度比其他竞争对手快得多。此外，使用统计显着性检验和生物学显着性检验来验证所获得的结果。获得的结果清楚地表明，与其他方法相比，我们的方法可提取高质量的双簇，并且收敛速度比其他竞争对手快得多。此外，使用统计显着性检验和生物学显着性检验来验证所获得的结果。