检索结果-内蒙古大学图书馆

Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

GENE 2012年第2期506卷 408-416页

作者： Vineetha, S. Bhat, C. Chandra Shekara Idicula, Sumam Mary Rajiv Gandhi Inst Technol Dept Comp Sci Kottayam Kerala India Natl Inst Interdisciplinary Sci & Technol Trivandrum Kerala India Cochin Univ Sci & Technol Cochin 682016 Kerala India

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy. (C) 2012 Elsevier B.V. All rights reserved.

关键词： microarray data Gene regulatory network Recurrent neural fuzzy network Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Gene selection for microarray data analysis using principal component analysis

引用

STATISTICS IN MEDICINE 2005年第13期24卷 2069-2087页

作者： Wang, AT Gehan, EA Georgetown Univ Vincent T Lombardi Canc Res Ctr Dept Biomath & Biostat Washington DC USA

Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray data analysis. For a typical microarray data set, it is often difficult to compare the overall gene expression difference between observations from different groups or conduct the classification based on a very large number of genes. In this paper, we propose a gene selection method based on the strategy proposed by Krzanowski. We demonstrate the effectiveness of this procedure using a cancer gene expression data set and compare it with several other gene selection strategies. It turns out that the proposed method selects the best gene subset for preserving the original data structure. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： microarray data principal component analysis procrustes criterion gene selection

来源：评论

学校读者我要写书评

暂无评论

MIDAW: a web tool for statistical analysis of microarray data

引用

NUCLEIC ACIDS RESEARCH 2005年第Web Server issue期33卷 W644-W649页

作者： Romualdi, C Vitulo, N Favero, MD Lanfranchi, G Univ Padua Dipartimento Biol CRIBI Biotechnol Ctr I-35121 Padua Italy

MIDAW (microarray data analysis web tool) is a web interface integrating a series of statistical algorithms that can be used for processing and interpretation of microarray data. MIDAW consists of two main sections: data normalization and data analysis. In the normalization phase the simultaneous processing of several experiments with background correction, global and local mean and variance normalization are carried out. The data analysis section allows graphical display of expression data for descriptive purposes, estimation of missing values, reduction of data dimension, discriminant analysis and identification of marker genes. The statistical results are organized in dynamic web pages and tables, where the transcript/ gene probes contained in a specific microarray platform can be linked ( according to user choice) to external databases (GenBank, Entrez Gene, UniGene). Tutorial files help the user throughout the statistical analysis to ensure that the forms are filled out correctly. MIDAW has been developed using Perl and PHP and it uses R/Bioconductor languages and routines. MIDAW is GPL licensed and freely accessible at http://***. ***/midaw/.Perl and PHP source codes are available from the authors upon request.

关键词： .discriminant data analysis normalization Statistical analysis microarray data expression data local mean missing values web tool MIDAW

来源：评论

学校读者我要写书评

暂无评论

A Novel Information Theoretic Approach to Gene Selection for Cancer Classification Using microarray data

引用

CURRENT BIOINFORMATICS 2015年第4期10卷 431-440页

作者： Naseem, Imran Togneri, Roberto Bennamoun, Mohammed Karachi Inst Econ & Technol Coll Engn Karachi Pakistan Univ Western Australia Sch Elect Elect & Comp Engn Crawley WA 6009 Australia Univ Western Australia Sch Comp Sci & Software Engn Crawley WA 6009 Australia

In this research an efficient gene selection method called Discriminant Mutual Information (DMI) algorithm is proposed. The DMI algorithm sequentially induces discrimination and relevance to identify the most significant genes for tumor classification. In particular, in the first step the entire gene population is decorrelated by the formation of gene-sets such that the genes with similar characteristics form a single gene-set. The mutual information criterion is further employed to identify the most representative gene of each gene-set. Extensive experiments have been conducted on six publicly available databases where the proposed DMI algorithm has shown good results compared to a number of state-of-the-art approaches. Extensive computational analysis clearly reflects the computational efficiency of the proposed approach, typically it requires only a few seconds for experimentation on standard microarray datasets.

关键词： Gene selection microarray data tumor classification

来源：评论

学校读者我要写书评

暂无评论

An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer

引用

ARTIFICIAL INTELLIGENCE IN MEDICINE 2008年第1期42卷 81-93页

作者： Lee, Zne-Jung Huafan Univ Dept Informat Management Shihding Township 22301 Taipei County Taiwan

Objective: The type of data in microarray provides unprecedented amount of data. A typical microarray data of ovarian cancer consists of the expressions of tens of thousands of genes on a genomic scale, and there is no systematic procedure to analyze this information instantaneously. To avoid higher computational complexity, it needs to select the most Likely differentially expressed gene markers to explain the effects of ovarian cancer. Traditionally, gene markers are selected by ranking genes according to statistics or machine learning algorithms. In this paper, an integrated algorithm is derived for gene selection and classification in microarray data of ovarian cancer. Methods: First, regression analysis is applied to find target genes. Genetic algorithm (GA), particle swarm optimization (PSO), support vector machine (SVM), and analysis of variance (ANOVA) are hybridized to select gene markers from target genes. Finally, the improved fuzzy model is applied to classify cancer tissues. Results: The microarray data of ovarian cancer, obtained from China Medical University Hospital, is used to test the performance of the proposed algorithm. In simulation, 200 target genes are obtained after regression analysis and six gene markers are selected from the hybrid process of GA, PCO, SVM and ANOVA. Additionally, these gene markers are used to classify cancer tissues. Conclusions: The proposed algorithm can be used to analyze gene expressions and has superior performance in microarray data of ovarian cancer, and it can be performed on other studies for cancer diagnosis. (C) 2007 Elsevier B.V. All rights reserved.

关键词： ovarian cancer microarray data gene selection support vector machine genetic algorithm particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Wavelet selection for disease classification by DNA microarray data

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第1期38卷 990-995页

作者： Nanni, Loris Lumini, Alessandra Univ Bologna DEIS IEIIT CNR I-40136 Bologna Italy

The microarrays report the measures of the expression levels of tens of thousands of genes, this high dimensional feature vector contains also irrelevant information for accurate classification. Moreover, only few training samples are available, hence for avoiding the curse of dimensionality problem a feature reduction should be performed before the classification step. Here, we proposed a set of orthogonal wavelet detail coefficients of different wavelet mothers to extract the features from the microarray data. We propose to use a multi-classifiers where each classifier, a support vector machine, is trained using a different set of detail coefficients, the classifiers are combined by "sum rule". The detail coefficients set selection is performed by running Sequential Forward Floating Selection (SFFS). The goodness of the proposed method is validated using the area under the ROC curve as performance indicator, the experiments are carried out on four-datasets: Breast dataset;Ovarian dataset;Lung dataset;Prostate dataset. The results show that the proposed method outperforms the performance that can be obtained by a single set of detail coefficients. Moreover, we have shown that, also using as features the detail coefficients, a random subspace of classifiers outperforms the stand-alone classifiers. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： microarray data 1-D wavelet transform Support vector machine Fusion of classifiers

来源：评论

学校读者我要写书评

暂无评论

Wavelet feature extraction for high-dimensional microarray data

引用

NEUROCOMPUTING 2009年第4-6期72卷 985-990页

作者： Liu, Yihui Shandong Inst Light Ind Sch Comp Sci & Informat Technol Jinan 250353 Peoples R China

It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition. Original microarray data are usually of high dimensionality, whereas, only limited training samples are available. Therefore, dimensionality reduction is an important strategy to greatly improve the classification performance of microarray data. A novel method of feature extraction and dimensionality reduction for high-dimensional microarray data is proposed in this study. A set of orthogonal wavelet detail coefficients based on wavelet decomposition at different levels is extracted to characterize the localized features of microarray data. Experiments are carried out on four datasets. A highly competitive accuracy is achieved in comparison with the performance of other models. (C) 2008 Elsevier B.V. All rights reserved.

关键词： microarray data Dimensionality reduction Feature extraction Wavelet analysis

来源：评论

学校读者我要写书评

暂无评论

Population characteristic exploitation-based multi-orientation multi-objective gene selection for microarray data classification

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2024年 170卷 108089-108089页

作者： Li, Min Cao, Rutun Zhao, Yangfan Li, Yulong Deng, Shaobo Nanchang Inst Technol Sch Informat Engn 289 Tianxiang Rd Nanchang Jiangxi Peoples R China

Gene selection is a process of selecting discriminative genes from microarray data that helps to diagnose and classify cancer samples effectively. Swarm intelligence evolution-based gene selection algorithms can never circumvent the problem that the population is prone to local optima in the process of gene selection. To tackle this challenge, previous research has focused primarily on two aspects: mitigating premature convergence to local optima and escaping from local optima. In contrast to these strategies, this paper introduces a novel perspective by adopting reverse thinking, where the issue of local optima is seen as an opportunity rather than an obstacle. Building on this foundation, we propose MOMOGS-PCE, a novel gene selection approach that effectively exploits the advantageous characteristics of populations trapped in local optima to uncover global optimal solutions. Specifically, MOMOGS-PCE employs a novel population initialization strategy, which involves the initialization of multiple populations that explore diverse orientations to foster distinct population characteristics. The subsequent step involved the utilization of an enhanced NSGA-II algorithm to amplify the advantageous characteristics exhibited by the population. Finally, a novel exchange strategy is proposed to facilitate the transfer of characteristics between populations that have reached near maturity in evolution, thereby promoting further population evolution and enhancing the search for more optimal gene subsets. The experimental results demonstrated that MOMOGS-PCE exhibited significant advantages in comprehensive indicators compared with six competitive multi-objective gene selection algorithms. It is confirmed that the "reverse-thinking" approach not only avoids local optima but also leverages it to uncover superior gene subsets for cancer diagnosis.

关键词： Gene selection microarray data Multi-orientation Multi-objective Reverse thinking

来源：评论

学校读者我要写书评

暂无评论

Regularized logistic regression without a penalty term: An application to cancer classification with microarray data

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 5110-5118页

作者： Bielza, Concha Robles, Victor Larranaga, Pedro Tech Univ Madrid Dept Artificial Intelligence Madrid Spain Tech Univ Madrid Dept Comp Architecture & Technol Madrid Spain

Regularized logistic regression is a useful classification method for problems with few samples and a huge number of variables. This regression needs to determine the regularization term, which amounts to searching for the optimal penalty parameter and the norm of the regression coefficient vector. This paper presents a new regularized logistic regression method based on the evolution of the regression coefficients using estimation of distribution algorithms. The main novelty is that it avoids the determination of the regularization term. The chosen simulation method of new coefficients at each step of the evolutionary process guarantees their shrinkage as an intrinsic regularization. Experimental results comparing the behavior of the proposed method with Lasso and ridge logistic regression in three cancer classification problems with microarray data are shown. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Logistic regression Regularization Estimation of distribution algorithms Cancer classification microarray data

来源：评论

学校读者我要写书评

暂无评论

A Unified Multi-Class Feature Selection Framework for microarray data

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第6期20卷 3725-3736页

作者： Ding, Xiaojian Yang, Fan Ma, Fumin Chen, Shilin Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210007 Peoples R China Nanjing Med Univ Jiangsu Canc Hosp Jiangsu Inst Canc Res Thorac SurgCanc Hosp Nanjing 211166 Jiangsu Peoples R China

In feature selection research, simultaneous multi-class feature selection technologies are popular because they simultaneously select informative features for all classes. Recursive feature elimination (RFE) methods are state-of-the-art binary feature selection algorithms. However, extending existing RFE algorithms to multi-class tasks may increase the computational cost and lead to performance degradation. With this motivation, we introduce a unified multi-class feature selection (UFS) framework for randomization-based neural networks to address these challenges. First, we propose a new multi-class feature ranking criterion using the output weights of neural networks. The heuristic underlying this criterion is that "the importance of a feature should be related to the magnitude of the output weights of a neural network". Subsequently, the UFS framework utilizes the original features to construct a training model based on a randomization-based neural network, ranks these features by the criterion of the norm of the output weights, and recursively removes a feature with the lowest ranking score. Extensive experiments on 15 real-world datasets suggest that our proposed framework outperforms state-of-the-art algorithms. The code of UFS is available at https://***/SVMrelated/***.

关键词： Feature extraction Training Support vector machines Task analysis Optimization Standards Radial basis function networks Multi-class feature selection randomization feature ranking criterion microarray data

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：