School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia.
BMC Med Genomics. 2019 Jan 15;12(1):10. doi: 10.1186/s12920-018-0447-6.
Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. Gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. Gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification results.
An innovative Gene Selection Programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on Gene Expression Programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. . Support Vector Machine (SVM) with a linear kernel serves as a classifier of the GSP.
Experimental results on ten microarray cancer datasets demonstrate that Gene Selection Programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection methods.
Gene subset selected by GSP can achieve a higher classification accuracy with less processing time.
微阵列数据集是一种重要的医学诊断工具，因为它们代表了细胞在分子水平上的状态。与涉及的大量基因相比，用于对癌症类型进行分类的可用微阵列数据集的样本量通常相当小。这一事实被称为维度诅咒，这是一个具有挑战性的问题。基因选择是一种有前途的方法，可以解决这个问题，并由于只有少数基因与分类问题相关，因此在开发有效的癌症分类方法中发挥着重要作用。基因选择解决了微阵列数据集中的许多问题，例如减少不相关和嘈杂的基因数量，并选择与分类问题最相关的基因，以提高分类结果。
提出了一种创新的基因选择编程（GSP）方法，用于选择相关基因以进行有效和高效的癌症分类。GSP 基于基因表达编程（GEP）方法，具有新定义的种群初始化算法、新的适应度函数定义以及改进的突变和重组算子。支持向量机（SVM）具有线性核作为 GSP 的分类器。
在十个微阵列癌症数据集上的实验结果表明，基因选择编程（GSP）在从微阵列数据集中消除不相关和冗余基因/特征方面是有效和高效的。与其他方法的综合评估和比较表明，GSP 在所有三个评估标准（即分类准确性、选择的基因数量和计算成本）方面都提供了更好的折衷。与最新的代表性基因选择方法相比，GSP 选择的基因集在癌症分类方面表现出了更好的性能。
GSP 选择的基因子集可以用更少的处理时间实现更高的分类准确性。
