A model for gene selection and classification of gene expression data

Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data which will maximize the classification accuracy. A model for gene selection and classification has been developed by using a filter approach, and an improved hybrid of the genetic algorithm and a support vector machine classifier. We show that the classification accuracy of the proposed model is useful for the cancer classification of one widely used gene expression benchmark data set.     

Particle swarm optimization with a modified sigmoid function for gene selection from gene expression data

In order to select a small subset of informative genes from gene expression data for cancer classification, many researchers have recently analyzed gene expression data using various computational intelligence methods. However, due to the small number of samples compared with the huge number of genes (high-dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties in selecting such a small subset. Therefore, we propose an enhancement of binary particle swarm optimization to select the small subset of informative genes that is relevant for classifying cancer samples more accurately. In this method, three approaches have been introduced to increase the probability of the bits in a particle’s position being zero. By performing experiments on two gene expression data sets, we have found that the performance of the proposed method is superior to previous related works, including the conventional version of binary particle swarm optimization (BPSO), in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared with BPSO.     

Gene subset selection using an iterative approach based on genetic algorithms

Microarray data are expected to be useful for cancer classification. However, the process of gene selection for the classification contains a major problem due to properties of the data such as the small number of samples compared with the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this article aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve this aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other previous related work, as well as to four methods tried in this work. In addition, a list of informative genes in the best gene subsets is also presented for biological usage.     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Particle swarm optimization for gene selection in classifying cancer classes

The application of microarray data for cancer classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contribute to a disease. This selection process is difficult due to the availability of a small number of samples compared with the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this article proposes an improved binary particle swarm optimization to select a near-optimal (small) subset of informative genes that is relevant for the cancer classification. Experimental results show that the performance of the proposed method is superior to the standard version of particle swarm optimization (PSO) and other previous related work in terms of classification accuracy and the number of selected genes.     

Gene selection using hybrid particle swarm optimization and genetic algorithm

Selecting high discriminative genes from gene expression data has become an important research. Not only can this improve the performance of cancer classification, but it can also cut down the cost of medical diagnoses when a large number of noisy, redundant genes are filtered. In this paper, a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. The proposed approach is tested on three benchmark gene expression datasets: Leukemia, Colon and breast cancer data. Experimental results show that the proposed method can reduce the dimensionality of the dataset, and confirm the most informative gene subset and improve classification accuracy.     

肿瘤信息基因启发式宽度优先搜索算法研究

基于基因表达谱的肿瘤检测方法有望成为临床医学上一种快速而有效的肿瘤分子诊断方法,但由于基因表达谱数据存在维数过高、样本量很小以及噪音很大等特点,使得肿瘤信息基因选择成为一件有挑战性的工作.根据肿瘤基因表达谱样本集的特点,提出了一种以支持向量机分类性能为评估准则的寻找信息基因的启发式宽度优先搜索算法,其优点是能够同时搜索到基因数量尽可能少而分类能力尽可能强的多个信息基因子集.实验采用了3种肿瘤样本集以验证新算法的可行性和有效性,对于急性白血病、难以分类的结肠癌和多肿瘤亚型的小圆蓝细胞瘤样本集,分别只需2,4和4个信息基因就能获得100%的4-折交叉验证识别准确率.与其它优秀的肿瘤分类方法相比,实验结果在信息基因数量及其分类性能方面具有明显的优越性.为避免样本集的不同划分对分类性能的影响,提出了一种能够更加客观地反映信息基因子集分类性能的全折交叉验证评估方法.     

多类别肿瘤基因表达谱的自动特征选择方法

从信息学角度出发寻找肿瘤相关基因、发现肿瘤基因表达特征对肿瘤的诊断和治疗具有重要的生物学意义,而肿瘤与正常组织的分类是其中一个重要应用。根据多类别肿瘤基因表达谱,提出了一种自动特征选择方法。首先,结合非参数方法和filter思想,利用决策序列的随机性度量基因的权值并排序;然后,采用相关信息熵进行冗余性排除,自动地选择出具有高分辨能力、低冗余度的特征基因子集。实验结果表明,提出的方法能从多类别肿瘤基因表达谱数据中自动选出30个具有良好分类能力的特征基因,且具有较高的正确识别率。     

Direct integration of microarrays for selecting informative genes and phenotype classification

The ability to provide thousands of gene expression values simultaneously makes microarray data very useful for phenotype classification. A major constraint in phenotype classification is that the number of genes greatly exceeds the number of samples. We overcame this constraint in two ways; we increased the number of samples by integrating independently generated microarrays that had been designed with the same biological objectives, and reduced the number of genes involved in the classification by selecting a small set of informative genes. We were able to maximally use the abundant microarray data that is being stockpiled by thousands of different research groups while improving classification accuracy. Our goal is to implement a feature (gene) selection method that can be applicable to integrated microarrays as well as to build a highly accurate classifier that permits straightforward biological interpretation. In this paper, we propose a two-stage approach. Firstly, we performed a direct integration of individual microarrays by transforming an expression value into a rank value within a sample and identified informative genes by calculating the number of swaps to reach a perfectly split sequence. Secondly, we built a classifier which is a parameter-free ensemble method using only the pre-selected informative genes. By using our classifier that was derived from large, integrated microarray sample datasets, we achieved high accuracy, sensitivity, and specificity in the classification of an independent test dataset.     

基于对称不确定性和SVM递归特征消除的信息基因选择方法*

基因表达谱中存在大量与肿瘤分类无关的基因,严重降低肿瘤诊断的准确率.基因表达谱还存在高维小样本、噪声大等问题,增加肿瘤诊断的难度.为了获取基因数量较少且分类能力较强的信息基因子集,文中提出基于对称不确定性(SU)和支持向量机递归特征消除(SVM-RFE)的信息基因选择方法.首先利用SU评估基因和类标签之间的相关性,根据SU定义近似马尔科夫毯,快速消除大量无关和冗余基因.然后利用SVM-RFE进一步剔除冗余基因,获取有效的信息基因子集.实验表明,文中方法可以在选取维数较少或相等的信息基因子集情况下获取较高的肿瘤分类性能.     

基于对称不确定性和邻域粗糙集的肿瘤分类信息基因选择

基因表达谱中信息基因选择是有效建立肿瘤分类模型的关键问题。肿瘤基因表达谱具有高维小样本、噪声大且存在大量无关和冗余基因等特点。为了获得基因数量尽可能少而分类能力尽可能强的一组信息基因,提出一种基于对称不确定性和邻域粗糙集的肿瘤分类信息基因选择SUNRS方法。首先利用对称不确定性指标评估信息基因的重要度,以剔除大量无关和冗余基因,获取信息基因的候选子集;然后利用邻域粗糙集约简算法对信息基因候选子集进行寻优,获得信息基因的目标子集。实验结果表明,SUNRS方法能够用较少的信息基因获得更高的分类精度,从而既能改善算法的泛化性能,又能提高时间效率。     

K-split Lasso:有效的肿瘤特征基因选择方法

随着DNA微阵列技术的出现,大量关于不同肿瘤的基因表达谱数据集被发布到网络上,从而使得对肿瘤特征基因选择和亚型分类的研究成为生物信息学领域的热点。基于Lasso(least absolute shrinkage and selection operator)方法提出了K-split Lasso特征选择方法,其基本思想是将数据集平均划分为K份,分别使用Lasso方法对每份进行特征选择,而后将选择出来的每份特征子集合并,重新进行特征选择,得到最终的特征基因。实验采用支持向量机作为分类器,结果表明K-split Lasso方法减少了冗余特征,提高了分类精度,具有良好的稳定性。由于每次计算的维数降低,K-split Lasso方法解决了计算开销过大的问题,并在一定程度上解决了"过拟合"问题。因此K-split Lasso方法是一种有效的肿瘤特征基因选择方法。     

Improving the prediction of the clinical outcome of breast cancer using evolutionary algorithms

There exist several methods for binary classification of gene expression data sets. However, in the majority of published methods, little effort has been made to minimize classifier complexity. In view of the small number of samples available in most gene expression data sets, there is a strong motivation for minimizing the number of free parameters that must be fitted to the data. In this paper, a method is introduced for evolving (using an evolutionary algorithm) simple classifiers involving a minimal subset of the available genes. The classifiers obtained by this method perform well, reaching 97% correct classification of clinical outcome on training samples from the breast cancer data set published by van't Veer, and up to 89% correct classification on validation samples from the same data set, easily outperforming previously published results.     

Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.     

基于支持向量机的肿瘤分类特征基因选取

依据基因表达谱有效建立肿瘤分类模型的关键在于准确找出决定样本类别的一组特征基因．针对该问题,在分析肿瘤基因表达谱特征的基础上,研究了肿瘤分类特征基因选取问题．首先,提出了一种新的类别可分性判据以滤除分类无关基因,并采用支持向量机作为分类器进行特征基因分类性能的检验．然后,采用两两冗余分析及基于支持向量机分类模型的灵敏度分析法进行冗余基因的剔除．以急性白血病亚型分类特征基因选取为例进行实验,结果表明了上述方法的可行性和有效性．     

基于基因表达谱的SRBCT分类研究

肿瘤亚型的准确判别对肿瘤的治疗具有重要的意义。文章提出了一种多类肿瘤分类和特征基因选取的策略。该方法以儿童SRBCT(小圆蓝细胞瘤)的基因表达谱为例,计算基因的类加权Bhattacharyya距离,并据此选取特征基因,然后利用这些基因的表达数据建立了基于支持向量机的多模预测模型并对SRBCT的4种亚型进行了识别。实验结果表明了该方法的有效性和可行性。     

考虑样本不平衡的模型无关的基因选择方法

在基因表达数据分析中,鉴别基因是后续研究中非常重要的信息基因.有很多研究致力于从基因表达数据中选出信息基因这一挑战性工作,并提出了一些基因选择方法.然而,这些方法(特别是非参数选择方法)都没有考虑不同样本类别中样本大小的不平衡性问题.考虑样本不平衡性和基因选择方法的稳定性,给出一个全新的与数据分布模型无关的基因选择方法.在类内变化小和类间差别大的策略下,选择敏感的度量函数提高方法的鉴别能力,同时,利用类内变化和类间差别的一致性来增加方法的稳定性和适用性.这一方法不但可以应用于两个类别的情况,也可以应用于多个类别的情况.最后,使用两组真实的基因表达数据对所提出的方法进行了验证.实验结果表明,这一方法比其他方法具有更高的有效性和稳健性.     

结肠癌患者诊断的基因标志物识别算法*

为了得到具有强分类信息的极少结肠癌特征基因,实现对结肠癌患者的准确识别,文中提出结肠癌患者诊断的基因标志物识别算法.首先提出基因密度和基因距离的概念,构造以基因密度和基因距离分别为横纵坐标的基因2D空间散列图,选择处于密度峰值点的基因构成优选基因子集,然后采用密度峰值K中心点(DP_K-medoids)算法对降维后的结肠数据集样本进行聚类分析.基因距离和样本距离分别采用欧氏距离、曼哈顿距离、切比雪夫距离和夹角余弦距离度量.实验表明,在夹角余弦距离下,文中算法可以选择到具有高准确率、高灵敏度、高特异度和高马修斯相关系数的规模较小的结肠癌基因子集.     

基于遗传算法的结肠癌基因选择与样本分类

提出了一种基于两轮遗传算法的用于结肠癌微阵列数据基因选择与样本分类的新方法。该方法先根据基因的Bhattacharyya距离指标过滤大部分与分类不相关的基因,而后使用结合了遗传算法和CFS（Correlation-based Feature Selection）的GA/CFS方法选择优秀基因子集,并存档记录这些子集。根据存档子集中基因被选择的频率选择进一步搜索的候选子集,最后以结合了遗传算法和SVM的GA/SVM从候选基因子集中选择分类特征子集。把这种GA/CFS-GA/SVM方法应用到结肠癌微阵列数据,实验结果及与文献的比较表明了该方法效果良好。     

Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data

BackgroundThe application of microarray data for cancer classification is important. Researchers have tried to analyze gene expression data using various computational intelligence methods.PurposeWe propose a novel method for gene selection utilizing particle swarm optimization combined with a decision tree as the classifier to select a small number of informative genes from the thousands of genes in the data that can contribute in identifying cancers.ConclusionStatistical analysis reveals that our proposed method outperforms other popular classifiers, i.e., support vector machine, self-organizing map, back propagation neural network, and C4.5 decision tree, by conducting experiments on 11 gene expression cancer datasets.