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引用次数: 6
摘要
医学诊断是当前机器学习和数据挖掘研究的一个主要领域。单核苷酸多态性(SNPs)是人类基因组变异的重要来源,因此与包括癌症在内的多种人类疾病有关。乳腺癌是女性最常见的恶性肿瘤,在过去二十年中已大面积扩散。为了将肿瘤样本与正常样本区分开来,人们在 SNP 数据上应用了各种技术。与 SNP 数据相关的主要问题之一是特征数量较多,这使得分类任务变得复杂。本文提出了一种基于关联规则挖掘(ARM)和神经网络(NN)的新型混合智能技术,该技术使用进化算法(EA)来处理乳腺癌诊断中的维度问题。通过语法进化(GE)优化的 ARM 用于选择信息量最大的特征,并通过提取 SNP 之间的关联来降低维度,而 NN 则用于高效分类。所提出的 NN-GEARM 方法已应用于从 NCBI 基因表达总库(GEO)网站获得的乳腺癌 SNP 数据集。创建的模型准确率高达 90%。
Classication of SNPs for breast cancer diagnosis using neural-network-based association rules
Medical diagnosis is a major area of current research in Machine Learning and Data Mining. Single Nucleotide Polymorphisms (SNPs) are an important source of the human genome variability and have thus been implicated in several human diseases, including cancer. Breast Cancer is the most common malignant tumour for women and has known a large spread during the past twenty years. To separate the tumorous samples from the normal ones, various techniques have been applied on SNP data. One of the major problems related to SNP data is the high number of features which makes the task of classification complex. In this paper, a new hybrid intelligent technique based on Association Rule Mining (ARM) and Neural Networks (NN) which uses an Evolutionary Algorithm (EA) is proposed to deal with the dimensionality problem for the diagnosis of breast cancer. ARM optimized by Grammatical Evolution (GE) is used to select the most informative features and reduce the dimensionality by extracting associations between SNPs, while NN is used for efficient classification. The proposed NN-GEARM approach has been applied on breast cancer SNP dataset obtained from the NCBI Gene Expression Omnibus (GEO) website. The created model has reached an accuracy of up to 90%.