Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

A Survey on Acute Leukemia Expression Data Classification Using Ensembles

Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.

Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.

Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted.

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

Multi-class cancer classification through gene expression profiles: microRNA versus mRNA

Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.

Cancer classification based on microarray gene expression data using a principal component accumulation method

The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.

基于P-tree的多决策树基因表达数据分类

提出基于P-tree的多决策树分类基因表达数据方法PTMDT(P-treemulti-decisiontree)。

Genome-wide analysis of the barley non-specific lipid transfer protein gene family

Non-specific lipid transfer proteins(nsLTPs) are small, basic proteins that are characterized by an eight-cysteine motif. The biological functions of these proteins have been reported to involve plant reproduction and biotic or abiotic stress response. With the completion of the barley genome sequence, a genome-wide analysis of nsLTPs in barley(Hordeum vulgare L.)(HvLTPs) will be helpful for understanding the function of nsLTPs in plants. We performed a genome-wide analysis of the nsLTP gene family in barley and identified 70 nsLTP genes,which can be divided into five types(1, 2, C, D, and G). Each type of nsLTPs shares similar exon and intron gene structures. Expression analysis showed that barley nsLTPs have diverse expression patterns, revealing their various roles. Our results shed light on the phylogenetic relationships and potential functions of barley nsLTPs and will be useful for future studies of barley development and molecular breeding.

Expression profiling-based clustering of healthy subjects recapitulates classifications defined by clinical observation in Chinese medicine

Differences between healthy subjects and associated disease risks are of substantial interest in clinical medicine. Based on clinical presentations, Traditional Chinese Medicine （TCM） classifies healthy people into nine constitutions： Balanced, Qi, Yang or Yin deficiency, Phlegm-dampness, Damp-heat, Blood stasis, Qi stagnation, and Inherited special constitutions. In particular, Yang and Yin deficiency constitutions exhibit cold and heat aversion, respectively. However, the intrinsic molecular characteristics of unbal- anced phenotypes remain unclear. To determine whether gene expression-based clustering can reca- pitulate TCM-based classification, peripheral blood mononudear cells （PBMCs） were collected from Chinese Han individuals with Yang/Yin deficiency （n = 12 each） and Balanced （n = 8） constitutions, and global gene expression profiles were determined using the Affymetrix HC-UI33A Plus 2.0 array. Notably, we found that gene expression-based classifications reflected distinct TCM-based subtypes. Consistent with the clinical observation that subjects with Yang deficiency tend toward obesity, series-clustering analysis detected several key lipid metabolic genes （diacylglycerol acyltransferase （DGAT2）, acyl-CoA synthetase （ACSL1）, and ATP-hinding cassette subfamily A member 1 （ABCAI）） to be down- and up- regulated in Yin and Yang deficiency constitutions, respectively. Our findings suggest that Yin]Yang deficiency and Balanced constitutions are unique entities in their mRNA expression profiles. Moreover, the distinct physical and clinical characteristics of each unbalanced constitution can be explained, in part, by specific gene expression signatures.

A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process.However,the high dimensionality of genetic data makes the classification process a challenging task.In this paper,we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm(simulated annealing(SA)),which will help select the most informative genes for breast cancer prediction.These optimal genes will then be used to train the classifier to improve its accuracy and efficiency.Three supervised machine-learning algorithms,namely,the support vector machine,the decision tree,and the random forest were used to create the classifier models that will help to predict breast cancer.Two different experiments were conducted using three datasets:Gene expression(GE),deoxyribonucleic acid(DNA)methylation,and a combination of the two.Six measures were used to evaluate the performance of the proposed algorithm,which include the following:Accuracy,precision,recall,specificity,area under the curve(AUC),and execution time.The effectiveness of the proposed classifiers was evaluated through comprehensive experiments.The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time.High accuracy values of 99.77%,99.45%,and 99.45%have been achieved by SA-SVM for GE,DNA methylation,and the combined datasets,respectively.The execution time of the proposed approach was significantly reduced,in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM,which was 0.02,0.03,and 0.02 on GE,DNA methylation,and the combined datasets respectively.In regard to precision and specificity,SA-RF obtained the best result of 100 on GE dataset.While SA-SVM attained the best recall result of 100 on GE dataset.

A new distributed feature selection technique for classifying gene expression data

Classification of gene expression data is a pivotal research area that plays a substantial role in diagnosis and prediction of diseases. Generally, feature selection is one of the extensively used techniques in data mining approaches, especially in classification. Gene expression data are usually composed of dozens of samples characterized by thousands of genes. This increases the dimensionality coupled with the existence of irrelevant and redundant features. Accordingly, the selection of informative genes (features) becomes difficult, which badly affects the gene classification accuracy. In this paper, we consider the feature selection for classifying gene expression microarray datasets. The goal is to detect the most possibly cancer-related genes in a distributed manner, which helps in effectively classifying the samples. Initially, the available huge amount of considered features are subdivided and distributed among several processors. Then, a new filter selection method based on a fuzzy inference system is applied to each subset of the dataset. Finally, all the resulted features are ranked, then a wrapper-based selection method is applied. Experimental results showed that our proposed feature selection technique performs better than other techniques since it produces lower time latency and improves classification performance.

基于混合进化算法的特征选择方法研究

特征选择(Feature Selection,FS)是一种有效的数据预处理方法,它可以通过选择高维数据中一组具有高相关性和低冗余性的特征,从而解决数据冗余引起的维数灾难.目前许多计算方法已经被应用于求解FS问题,其中基于教与学优化(Teaching and Learning-based Optimization Algorithm,TLBO)的特征选择模型由于其高效的全局搜索能力受到越来越多学者的关注.然而,随着数据规模的不断扩大,这些算法所具有的模型不稳定、模型精确度低和局部搜索能力差等局限性,使算法的研究逐步陷入困境.为解决上述问题,本文提出了融合教与学优化算法与局部搜索方法(Local Search,LS)的混合进化Wrapper算法模型(Teaching and Learning-based Optimization-Local Search Algorithm,TLBOLS).首先,由于传统的教与学优化算法不能直接用于求解特征选择问题,算法在初始化阶段将实数型编码转为二进制编码,然后为保证种群的多样性,在教阶段引入最差个体重启机制,并针对进化班级过程中学习者与教学者两种身份采用不同值的TF值,提出二进制的教与学特征选择算法(Binary Teaching and Learning-based Optimization-Local Search Algorithm,BTLBOLS).随后,提出结合多操作的局部搜索方法和变邻域搜索逐渐增强扰动力度,提高整个种群的个体质量.为优化特征选择结果,BTLBOLS利用综合评价指标作为目标函数指导整体进化过程.实验选取45个高维癌症基因表达数据集进行测试并与十种特征选择算法相比,实验结果表明,相比其他算法,BTLBOLS在分类准确率和特征个数上都具有一定优势,算法分类性能有效提高.

Molecular subtypes identified by gene expression profiling in early stage endometrioid endometrial adenocarcinoma

Background Early stage （FIGO stage Ⅰ-Ⅱ） endometrioid endometrial adenocarcinoma （EEA） is very common in clinical practice.However,patients with the early stage EEA show various clinical behaviors due to biological heterogeneity.Hence,we aimed to discover distinct classes of tumors based on gene expression profiling,and analyze whether the molecular classification correlated with the histopathological stages or other clinical parameters.Methods Hierarchical clustering was performed for class discovery in 28 eady stage EEA samples using a special cDNA microarray chip containing 492 genes designed for endometrial cancer.Correlations between clinicopathologic parameters and our classification were analyzed.And the significance analysis of microarrays （SAM） array was used to identify the signature genes according to the tumor grade and myometrial invasion.Results Three tumor subtypes （subtypes Ⅰ,Ⅱ and Ⅲ） were identified by hierarchical clustering,each subtype had different clinicopathological factors,such as tumor grade,myometrial invasion status,and FIGO stage.Moreover,SAM analysis showed 34 up-regulated genes in high grade tumors,and 38 up-regulated genes and 1 down-regulated in deep myometrial invasive tumors.The overlap genes between these two high-risk factors were markedly up-regulated in subtype Ⅰ,but down-regulated in subtype Ⅲ.Conclusion We have identified novel molecular subtypes in early stage EEA.Differential gene signatures characterize each tumor subtype,which could be used for recognizing the tumor risk and providing a basis for further treatment stratification.

Analysis of Pathway Activity in Primary Tumors and NCI60 Cell Lines Using Gene Expression Profiling Data

To determine cancer pathway activities in nine types of primary tumors and NCI60 cell lines, we applied an in silico approach by examining gene signatures reflective of consequent pathway activation using gene expression data. Supervised learning approaches predicted that the Ras pathway is active in -70% of lung adenocarcinomas but inactive in most squamous cell carcinomas, pulmonary carcinoids, and small cell lung carcinomas. In contrast, the TGF-β, TNF-α, Src, Myc, E2F3, and β-catenin pathways are inactive in lung adenocarcinomas. We predicted an active Ras, Myc, Src, and/or E2F3 pathway in significant percentages of breast cancer, colorectal carcinoma, and gliomas. Our results also suggest that Ras may be the most prevailing oncogenic pathway. Additionally, many NCI60 cell lines exhibited a gene signature indicative of an active Ras, Myc, and/or Src, but not E2F3, β-catenin, TNF-α, or TGF-β pathway. To our knowledge, this is the first comprehensive survey of cancer pathway activities in nine major tumor types and the most widely used NCI60 cell lines. The ＂gene expression pathway signatures＂ we have defined could facilitate the understanding of molecular mechanisms in cancer development and provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screening.

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.

活动期和稳定期类风湿性关节炎寒热证候患者外周血CD_4^+T淋巴细胞基因表达谱探索

目的探讨活动期和稳定期类风湿性关节炎(RA)寒热证候的基因表达差异。方法采集RA寒热证候患者及正常人空腹静脉血,纯化得到CD4+T淋巴细胞,利用基因芯片检测和分析技术,探索活动期和稳定期RA寒热证候患者CD4+T淋巴细胞基因表达差异点。结果RA活动期和稳定期患者有63条基因表达存在显著性差异,主要涉及免疫应答;稳定期RA寒热证候患者之间有48条基因异常表达,只有1条与上述63条基因重复,主要涉及功能代谢;活动期RA寒热证候患者之间有59条基因异常表达,没有与上述稳定期与活动期比较的63条和稳定期寒热证候之间的48条基因重复,主要涉及功能代谢。结论RA患者稳定期和活动期之间的基因表达谱差异与寒热证候之间的基因表达差异有所不同,提示中医证候分类学具有基因表达谱依据。

基于输出不一致测度的极限学习机集成的基因表达数据分类

选择性集成学习已经成为分析基因表达数据、获取生物学信息的有力工具.为了更好地挖掘基因表达数据,利用极限学习机的集成,克服单个ELM用于数据分类时性能欠稳定的缺点,文中提出了一种基于输出不一致测度的ELM相异性集成算法(D-D-ELM).算法首先以输出不一致测度为标准对多个ELM模型进行相异性判断,其次根据ELM的平均分类精度剔除掉相应的模型,最后对筛选后的分类模型用多数投票法进行集成.算法被运用到Breast、Leukemia、Colon、Heart基因表达数据上,并通过理论和实验得到验证.实验结果的统计学分析表明D-D-ELM能够以更少的模型数量达到较稳定的分类精度.

寒热证候类风湿性关节炎患者外周血CD_4^+T淋巴细胞基因表达谱初步探索

目的:探讨类风湿性关节炎寒热证候的基因表达差异。方法:采集类风湿性关节炎寒热证候患者及正常人空腹静脉血,纯化得到CD4+T淋巴细胞,利用基因芯片检测和分析技术,探索类风湿性关节炎寒热证候患者及正常人CD4+T淋巴细胞基因表达差异点。结果:与正常人相比,类风湿性关节炎患者有149条基因异常表达,主要涉及免疫应答和信号传导;寒热证类风湿性关节炎患者之间有42条基因异常表达,只有2条与上述149条基因重复,主要涉及功能代谢、信号传导;寒热证类风湿性关节炎患者与正常人之间有49条基因异常表达,与上述42条之间有20条基因重复,也主要涉及功能代谢、信号传导;这些差异表达基因涉及多个生物学途径。结论:寒热证候类风湿性关节炎患者的基因表达谱存在差异,这种差异与类风湿性关节炎患者和正常人之间的差异有所不同,提示中医证候分类学具有基因表达谱依据。