Pathway-based gene selection for disease classification

M. Ibrahim, S. Jassim, M. Cawthorne, K. Langlands
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引用次数: 6

Abstract

The identification of disease biomarkers from genetic data such as high-throughput transcriptional profiling screens has attracted a great deal of recent interest due to relevance in prognostication and drug discovery. Biomarker discovery can be modelled as a feature selection problem that aims to find the most discriminating features (genes) for accurate disease classification e.g. healthy vs. diseased samples. Typical feature selection algorithms identify individual genes, and the disease discrimination power of each gene is considered separately. In this paper, we propose a gene selection method incorporating prior biological knowledge about gene pathways to find a group(s) of strongly correlated genes to accurately discriminate complex as well as simple diseases. The proposed method involves a ranking process to identify the most relevant biological pathways in a microarray dataset. A specified number of differentially- expressed genes from relevant pathways is then selected for accurate disease classification. The advantage of this method is that it searches for a group of strongly correlated genes rather than individual genes. We argue that selecting a group of informative and correlated genes from disease-associated pathways is particularly relevant to the disease type or grade. To evaluate the performance of our method, we compare it to five well-known feature selection and ranking methods using two classifiers: K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). Using publicly-available microarray datasets, we found that our algorithm outperforms other methods in terms of disease classification accuracy. Moreover, we were able to reduce the number of genes required to accurately discriminate disease states.
基于途径的疾病分类基因选择
从遗传数据中识别疾病生物标志物,如高通量转录谱筛选,由于与预后和药物发现相关,最近引起了人们的极大兴趣。生物标志物的发现可以建模为一个特征选择问题,旨在找到最具区别性的特征(基因),以进行准确的疾病分类,例如健康与患病样本。典型的特征选择算法识别单个基因,并单独考虑每个基因的疾病识别能力。在本文中,我们提出了一种基因选择方法,结合基因通路的先验生物学知识,找到一组强相关基因,以准确区分复杂和简单的疾病。提出的方法包括一个排序过程,以确定微阵列数据集中最相关的生物学途径。然后从相关途径中选择特定数量的差异表达基因进行准确的疾病分类。这种方法的优点是它搜索一组强相关基因而不是单个基因。我们认为,从疾病相关途径中选择一组信息丰富且相关的基因与疾病类型或等级特别相关。为了评估我们的方法的性能,我们将其与使用两种分类器的五种知名的特征选择和排序方法进行了比较:k -最近邻(KNN)和支持向量机(SVM)。使用公开可用的微阵列数据集,我们发现我们的算法在疾病分类准确性方面优于其他方法。此外,我们能够减少准确区分疾病状态所需的基因数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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