Abstract The analysis of omics data together with knowledge-based interpretation can help obtaining important information regarding different biological processes and to reflect the current physiological status of tissue and cells. The main challenge, however, is to analyze high-dimensional gene expression data consisting of a massive amount of redundant genes in extracting disease-related information. To address this problem, gene selection, that eliminates redundant and irrelevant genes, has been a key step. In current article, a feature selection technique is proposed that exploit correlation based overlapping analysis of expression data across classes. The proposed correlation based overlapping score (COS) technique is compared with state-of-the-art gene selection approaches using real-world benchmark microarray datasets. In an experimental evaluation, the COS algorithm outperforms the other methods with minimum misclassification errors obtained via boosting, random forest and k-nearest neighbour (kNN) classifiers. Moreover, the proposed technique is more stable than the other techniques in gene selection.

中文翻译：

---

基于 Chou 的 5 步规则，使用基于新的相关性重叠评分方法对基因表达数据进行特征选择和分类

摘要 组学数据分析和基于知识的解释有助于获取有关不同生物过程的重要信息，并反映组织和细胞的当前生理状态。然而，主要挑战是在提取疾病相关信息时分析由大量冗余基因组成的高维基因表达数据。为了解决这个问题，消除冗余和不相关基因的基因选择是关键的一步。在当前文章中，提出了一种特征选择技术，该技术利用基于相关性的跨类表达数据的重叠分析。将所提出的基于相关性的重叠评分 (COS) 技术与使用真实世界基准微阵列数据集的最先进的基因选择方法进行比较。在实验评估中，COS 算法在通过提升、随机森林和 k 近邻 (kNN) 分类器获得的最小误分类错误方面优于其他方法。此外，所提出的技术在基因选择方面比其他技术更稳定。