检索结果-内蒙古大学图书馆

Feature Selection of microarray data Using Simulated Kalman Filter with Mutation

PROCESSES 2023年第8期11卷

作者： Zamri, Nurhawani Ahmad Aziz, Nor Azlina Ab Bhuvaneswari, Thangavel Aziz, Nor Hidayati Abdul Ghazali, Anith Khairunnisa Multimedia Univ Fac Engn & Technol Melaka 75450 Malaysia

microarrays have been proven to be beneficial for understanding the genetics of disease. They are used to assess many different types of cancers. Machine learning algorithms, like the artificial neural network (ANN), can be trained to determine whether a microarray sample is cancerous or not. The classification is performed using the features of DNA microarray data, which are composed of thousands of gene values. However, most of the gene values have been proven to be uninformative and redundant. Meanwhile, the number of the samples is significantly smaller in comparison to the number of genes. Therefore, this paper proposed the use of a simulated Kalman filter with mutation (SKF-MUT) for the feature selection of microarray data to enhance the classification accuracy of ANN. The algorithm is based on a metaheuristics optimization algorithm, inspired by the famous Kalman filter estimator. The mutation operator is proposed to enhance the performance of the original SKF in the selection of microarray features. Eight different benchmark datasets were used, which comprised: diffuse large b-cell lymphomas (DLBCL);prostate cancer;lung cancer;leukemia cancer;"small, round blue cell tumor" (SRBCT);brain tumor;nine types of human tumors;and 11 types of human tumors. These consist of both binary and multiclass datasets. The accuracy is taken as the performance measurement by considering the confusion matrix. Based on the results, SKF-MUT effectively selected the number of features needed, leading toward a higher classification accuracy ranging from 95% to 100%.

关键词： feature selection simulated Kalman filter microarray data classification mutation

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Filter/Wrapper Feature Selection Approach Based on Improved Fruit Fly Optimization Algorithm and Chi-square Test for High Dimensional microarray data

引用

CURRENT BIOINFORMATICS 2021年第1期16卷 63-79页

作者： Yan, Chaokun Wu, Bin Ma, Jingjing Zhang, Ge Luo, Junwei Wang, Jianlin Luo, Huimin Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China China Mobile Online Serv Co Ltd Hong Kong Peoples R China Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Henan Peoples R China

microarray data is widely utilized for disease analysis and diagnosis. However, it is hard to process them directly and achieve high classification accuracy due to the intrinsic characteristics of high dimensionality and small size samples. As an important data preprocessing technique, feature selection is usually used to reduce the dimensionality of some datasets. Given the limitations of employing filter or wrapper approaches individually for feature selection, in the study, a novel hybrid filter-wrapper approach is proposed for high dimensional datasets. First, the Chi-square Test is utilized to filter out some irrelevant or redundant features. Next, an improved binary Fruit Fly Optimization algorithm is conducted to further search the optimal feature subset without degrading the classification accuracy. Here, the KNN classifier with the 10-fold-CV is utilized to evaluate the classification accuracy. Extensive experimental results on six benchmark biomedical datasets show that our proposed approach can achieve superior performance compared with other state-of-the-art methods.

关键词： Feature selection fruit fly optimization algorithm Chi-square test levy flight gaussian mutation microarray data

来源：评论

学校读者我要写书评

暂无评论

A new multi-objective binary Harris Hawks optimization for gene selection in microarray data

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第4期14卷 3157-3176页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France Lab Informat & Its Applicat Msila LIAM Msila Algeria

Cancer classification is one of the main applications of gene expression data (microarray data) and is essential for a comprehensive diagnosis of cancer treatment. Therefore, bio-inspired algorithms have developed several effective applications in the analysis of gene selection, which are one of the most effective applied in this domain. Harris Hawks optimization is a novel and recent algorithm that has an excellent balance between exploration and exploitation. This paper presents the first study on multi-objective binary Harris Hawks optimization (MOBHHO) for gene selection. We define gene selection as a problem, including two main conflicting objectives: minimizing the number of genes and maximizing the classification accuracy. MOBHHO uses two fitness functions to solve competing objectives. The first function based on SVM with LOOCV classifier and the second function also depends on KNN with K-fold classifier, as well as the percentage of gene selection found in both functions. Furthermore, MOBHHO tries to find the Pareto-optimal solutions, i.e. the best gene subset that contains a minimal number of selected genes and better classification accuracy. We have integrated several filter-based ranking methods with our proposal. In order to test the performance accuracy of the proposed MOBHHO algorithm, we compared our algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on eight benchmarks (binary-class and multi-class), MOBHHO able to provide a minimum number of genes to obtain the highest classification accuracy. The proposed method reaches above 98% classification accuracy in six benchmark datasets and a maximum accuracy of 100% is achieved.

关键词： Gene expression Feature selection Harris Hawks optimization algorithm microarray data Cancer classification Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2731-2750页

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test Q

关键词： Gene expression Feature selection Moth flame optimization algorithm Quantum computing microarray data Cancer classification Bio-inspired algorithms Molecular biology Optimization algorithms Evolutionary algorithms Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期161卷 107059-107059页

作者： Sadaqat, Muhammad Qasim, Muhammad ul Qamar, Muhammad Tahir Masoud, Muhammad Shareef Ashfaq, Usman Ali Noor, Fatima Fatima, Kinza Allemailem, Khaled S. Alrumaihi, Faris Almatroudi, Ahmad Govt Coll Univ Faisalabad GCUF Dept Bioinformat & Biotechnol Faisalabad 38000 Pakistan Qassim Univ Coll Appl Med Sci Dept Med Labs Buraydah 51452 Saudi Arabia

Liver cancer is a malignant tumor that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer. A list of studies evidences the therapeutic efficacy of Bacopa monnieri against liver cancer, but the precise molecular mechanism is yet to be discovered. This study combines data mining, network pharmacology, and molecular docking analysis to potentially revolutionize liver cancer treatment by identifying effective phytochemicals. Initially, the information on active constituents of B. monnieri and target genes of both liver cancer and B. monnieri were retrieved from literature as well as from publicly available databases. Based on the matching results between B. monnieri potential targets and liver cancer targets, the protein-protein interaction (PPI) network was constructed using the STRING database and imported into Cytoscape for screening of hub genes based on their degree of connectivity. Later, the interactions network between compounds and overlapping genes was constructed using Cytoscape software to analyze the network pharmacological prospective effects of B. monnieri on liver cancer. Gene Ontology (GO) and KEGG pathway analysis of hub genes revealed that these genes are involved in the cancer-related pathway. Lastly, the expression level of core targets was analyzed using microarray data (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790). Further, the GEPIA server and PyRx software were used for survival and molecular docking analysis, respectively. In summary, we proposed that quercetin, luteolin, apigenin, catechin, epicatechin, stigmasterol, beta-sitosterol, celastrol, and betulic acid inhibit tumor growth by affecting tumor protein 53 (TP53), interleukin 6 (IL6), RAC-alpha serine/threonine protein kinases 1 (AKT1), caspase-3 (CASP3), tumor necrosis factor (TNF), jun p

关键词： Liver cancer Bacopa monnieri Network pharmacology Hub genes microarray data Survival analysis Molecular docking

来源：评论

学校读者我要写书评

暂无评论

Gene selection of microarray data using Heatmap Analysis and Graph Neural Network

引用

APPLIED SOFT COMPUTING 2023年 135卷

作者： Pati, Soumen Kumar Banerjee, Ayan Manna, Sweta Maulana Abul Kalam Azad Univ Technol Dept Bioinformat Nadia 741249 W Bengal India Jalpaiguri Govt Engn Coll Dept Comp Sci & Engn Jalpaiguri 735102 W Bengal India

It is not feasible to investigate the whole genes at a microscopic level for disease classification in Genomics. It might take substantial time to execute any meaningful analysis and the computational resources will be misused as not all the genes are responsible for the disease linked to a cell. Currently, it is quite challenging to select the most significant genes from high-dimensional microarray data for disease classification. In search of a better process, a novel gene subset selection technique has been developed based on Heatmap Analysis and Graph Neural Network (HAGNN). In the proposed method, a heatmap analysis has been performed for the different classes of microarray data to obtain the Region of Interest (ROIs). These ROIs are extracted from the original dataset and undergo a node reduction technique followed by an edge reduction technique in Graph Neural Network (GNN). This paper is concluded with an optimal subset of the most significant genes that cause cancer. The popular base classifiers have been used to evidence the importance of the selected genes as compared to the original data with the help of several metrics. The obtained results clearly show that the proposed methodology outperformed the other existing methods and make a greater impact on the advancement of the GNN-based gene selection method.(c) 2023 Elsevier B.V. All rights reserved.

关键词： microarray data Gene selection Heatmap Analysis Graph Neural Network Drug discovery Laplacian matrix

来源：评论

学校读者我要写书评

暂无评论

GAMB-GNN: Graph Neural Networks learning from gene structure relations and Markov Blanket ranking for cancer classification in microarray data

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年 232卷

作者： Zhang, Shoujia Xie, Weidong Li, Wei Wang, Linjie Feng, Chaolu Northeastern Univ Coll Comp Sci & Engn 195 Chuangxin Rd Shenyang 110000 Liaoning Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image MIIC Minist Educ 195 Chuangxin Rd Shenyang 110000 Liaoning Peoples R China 195 Chuangxin Rd Shenyang 110169 Peoples R China

microarray data plays a significant role in the classification and prediction of cancer, which has become an essential area of research in computational biology and bioinformatics. Several machine learning methods have been applied to help classify and predict cancer using microarray data. However, previous methods have mainly leveraged the expression level of a subset of genes as a sample feature. These methods lack the utilization of the structure relation information among genes. At the same time, there is a lack of analysis of redundancy among features in the existing gene selection algorithms. This paper proposes a Graph Neural Networks model with the Markov Blanket ranking method for cancer classification (GAMB-GNN) in microarray data to combat these chal-lenges. The problem of cancer classification in the microarray data is considered by gene attributes and multi-type relation networks among genes in our model. The proposed GAMB-GNN obtains the gene scores and rank -ing according to the relevance of genes and cancer by the gene ranking algorithm based on Markov Blanket and then selects a top fraction of the ranking list to construct the multi-type gene relations graph. Further, GAMB-GNN employs a graph classifier with a relation attention mechanism and node aggregation pooling to learn the multiple relations structure and weight to obtain a better feature representation of the sample. The proposed model is validated through experiments in six public microarray datasets. The experiment results show that the accuracy and f1-score of our method on the cancer classification task on all tested microarray datasets improved by 3.98%-24.36% and 4.22%-31.93% on average over the baseline and state-of-the-art methods.

关键词： Cancer classification microarray data Graph Neural Networks Markov Blanket Feature selection Graph classification

来源：评论

学校读者我要写书评

暂无评论

Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data

引用

BMC BIOINFORMATICS 2023年第1期24卷 139-139页

作者： Wang, Zixuan Zhou, Yi Takagi, Tatsuya Song, Jiangning Tian, Yu-Shi Shibuya, Tetsuo Univ Tokyo Inst Med Sci Human Genome Ctr Div Med Data Informat Tokyo 1088639 Japan Peking Univ Beijing Int Ctr Math Res Beijing 100871 Peoples R China Osaka Univ Grad Sch Pharmaceut Sci 1-6 YamadaokaSuita Osaka 5650871 Japan Monash Univ Biomed Discovery Inst Melbourne Vic 3800 Australia Monash Univ Monash Data Futures Inst Melbourne Vic 3800 Australia

Backgroundmicroarray data have been widely utilized for cancer classification. The main characteristic of microarray data is "large p and small n" in that data contain a small number of subjects but a large number of genes. It may affect the validity of the classification. Thus, there is a pressing demand of techniques able to select genes relevant to cancer *** study proposed a novel feature (gene) selection method, Iso-GA, for cancer classification. Iso-GA hybrids the manifold learning algorithm, Isomap, in the genetic algorithm (GA) to account for the latent nonlinear structure of the gene expression in the microarray data. The Davies-Bouldin index is adopted to evaluate the candidate solutions in Isomap and to avoid the classifier dependency problem. Additionally, a probability-based framework is introduced to reduce the possibility of genes being randomly selected by GA. The performance of Iso-GA was evaluated on eight benchmark microarray datasets of cancers. Iso-GA outperformed other benchmarking gene selection methods, leading to good classification accuracy with fewer critical genes *** proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.

关键词： Cancer classification microarray data Gene selection Genetic algorithm Manifold algorithm

来源：评论

学校读者我要写书评

暂无评论

SEQENS: An ensemble method for relevant gene identification in microarray data

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2023年 152卷 106413-106413页

作者： Signol, Francois Arnal, Laura Navarro-Cerdan, J. Ramon Llobet, Rafael Arlandis, Joaquim Perez-Cortes, Juan-Carlos Univ Politecn Valencia Inst Tecnol Informat ITI Camino Vera S-N Valencia 46022 Spain

This paper describes an ensemble feature identification algorithm called SEQENS, and measures its capability to identify the relevant variables in a case-control study using a genetic expression microarray dataset. SEQENS uses Sequential Feature Search on multiple sample splitting to select variables showing stronger relation with the target, and a variable relevance ranking is finally produced. Although designed for feature identification, SEQENS could also serve as a basis for feature selection (classifier optimisation). Cliff, a ranking evaluation metric is also presented and used to assess the feature identification algorithms when a groundtruth of relevant variables is available. To test performance, three types of synthetic groundtruths emulating fictitious diseases are generated from ten randomly chosen variables following different target pattern distributions using the E-MTAB-3732 dataset. Several sample-to-dimensionality ratios ranging from 300 to 3,000 observations and 854 to 54,675 variables are explored. SEQENS is compared with other feature selection or identification state-of-the-art methods. On average, the proposed algorithm identifies better the relevant genes and exhibits a stronger stability. The algorithm is available to the community.

关键词： Gene identification Feature selection Ensemble method microarray data High dimensionality spaces

来源：评论

学校读者我要写书评

暂无评论

Comparison and Evaluation of the Combinations of Feature Selection and Classifier on microarray data 6

Comparison and Evaluation of the Combinations of Feature Sel...

引用

IEEE 6th International Conference on Big data Analytics (ICBDA)

作者： Yan, Chaokun Zhang, Jun Kang, Xi Gong, Zhengze Wang, Jianlin Zhang, Ge Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China

ISBN: (纸本)9781665415132

As gene chip technology is widely used in cancer research, a large number of valuable microarray data has been rapidly accumulated. These data have the characteristics of "high-dimensional small samples", in which most genes are unrelated or redundant. For high-dimensional, small-sample, high-noise, and few-sample binary classification datasets, we explore which combination of feature selection method and classifier can achieve the relatively best prediction accuracy, while the number of features included is relatively low. We adopt the standard data analysis procedures: preprocessing the data set, using different feature selection methods to generate feature subsets, and applying different classifiers to predict each feature subset. The results are compared to find out which combination with the relatively high prediction accuracy and the relatively small number of features.

关键词： Cancer classification prediction microarray data data analysis Feature selection Classification prediction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：