检索结果-内蒙古大学图书馆

A probabilistic relaxation labeling framework for reducing the noise effect in geometric biclustering of gene expression data

引用

PATTERN RECOGNITION 2009年第11期42卷 2578-2588页

作者： Zhao, Hongya Chan, Kwok Leung Cheng, Lee-Ming Yan, Hong City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Biclustering is an important method in DNA microarray analysis which can be applied when only a subset of genes is co-expressed in a subset of conditions. Unlike standard clustering analyses, biclustering methodology can perform simultaneous classification on two dimensions of genes and conditions in a microarray data matrix. However, the performance of biclustering algorithms is affected by the inherent noise in data, types of biclusters and computational complexity. In this paper, we present a geometric biclustering method based on the Hough transform and the relaxation labeling technique. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometric interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our algorithm, the Hough transform is employed for hyperplane detection in sub-spaces to reduce the computational complexity. Then sub-biclusters are combined into larger ones under the probabilistic relaxation labeling framework. Our simulation studies demonstrate the robustness of the algorithm against noise and outliers. In addition, our method is able to extract biologically meaningful biclusters from real microarray gene expression data. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： gene expression data analysis Clustering Biclustering Hough transform Probabilistic relaxation labeling

来源：评论

学校读者我要写书评

暂无评论

Biomarker Identification and Cancer Classification Based on Microarray data Using Laplace Naive Bayes Model with Mean Shrinkage

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第6期9卷 1649-1662页

作者： Wu, Meng-Yun Dai, Dao-Qing Shi, Yu Yan, Hong Zhang, Xiao-Fei Sun Yat Sen Univ Ctr Comp Vis Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Dept Math Guangzhou 510275 Guangdong Peoples R China Zhengzhou Normal Univ Sch Math & Stat Zhengzhou 450044 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Biomarker identification and cancer classification are two closely related problems. In gene expression data sets, the correlation between genes can be high when they share the same biological pathway. Moreover, the gene expression data sets may contain outliers due to either chemical or electrical reasons. A good gene selection method should take group effects into account and be robust to outliers. In this paper, we propose a Laplace naive Bayes model with mean shrinkage (LNB-MS). The Laplace distribution instead of the normal distribution is used as the conditional distribution of the samples for the reasons that it is less sensitive to outliers and has been applied in many fields. The key technique is the L-1 penalty imposed on the mean of each class to achieve automatic feature selection. The objective function of the proposed model is a piecewise linear function with respect to the mean of each class, of which the optimal value can be evaluated at the breakpoints simply. An efficient algorithm is designed to estimate the parameters in the model. A new strategy that uses the number of selected features to control the regularization parameter is introduced. Experimental results on simulated data sets and 17 publicly available cancer data sets attest to the accuracy, sparsity, efficiency, and robustness of the proposed algorithm. Many biomarkers identified with our method have been verified in biochemical or biomedical research. The analysis of biological and functional correlation of the genes based on gene Ontology (GO) terms shows that the proposed method guarantees the selection of highly correlated genes simultaneously.

关键词： Biomarker identification cancer classification Laplace distribution L-1 penalty gene expression data analysis

来源：评论

学校读者我要写书评

暂无评论

Parallelized Evolutionary Learning for Detection of Biclusters in gene expression data

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第2期9卷 560-570页

作者： Huang, Qinghua Tao, Dacheng Li, Xuelong Liew, Alan Wee-Chung S China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Guangdong Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ctr Quantum Computat & Intelligent Syst Sydney NSW 2007 Australia Chinese Acad Sci Ctr Opt IMagery Anal & Learning OPTIMAL State Key Lab Transient Opt & Photon Xian Inst Opt & Precis Mech Xian 710119 Peoples R China Griffith Univ Sch Informat & Commun Technol Gold Coast Qld 4222 Australia

The analysis of gene expression data obtained from microarray experiments is important for discovering the biological process of genes. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of experimental conditions. In this paper, we propose a new biclustering algorithm based on evolutionary learning. By converting the biclustering problem into a common clustering problem, the algorithm can be applied in a search space constructed by the conditions. To further reduce the size of the search space, we randomly separate the full conditions into a number of condition subsets (subspaces), each of which has a smaller number of conditions. The algorithm is applied to each subspace and is able to discover bicluster seeds within a limited computing time. Finally, an expanding and merging procedure is employed to combine the bicluster seeds into larger biclusters according to a homogeneity criterion. We test the performance of the proposed algorithm using synthetic and real microarray data sets. Compared with several previously developed biclustering algorithms, our algorithm demonstrates a significant improvement in discovering additive biclusters.

关键词： Biclustering genetic learning subdimensional search strategy gene expression data analysis

来源：评论

学校读者我要写书评

暂无评论

Fuzzy clustering analysis of microarray data

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE 2008年第H7期222卷 1143-1148页

作者： Han, Lixin Zeng, Xiaoqin Yan, Hong Hohai Univ Dept Comp Sci & Engn Nanjing 210008 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Fuzzy clustering is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy clustering method for microarray data analysis. An advantage of the method is that it used a combination of the fuzzy c-means and the principal component analysis to identify the groups of genes that show similar expression patterns. It allows a gene to belong to more than a gene expression pattern with different membership grades. The method is suitable for the analysis of large amounts of noisy microarray data.

关键词： gene expression data analysis fuzzy clustering fuzzy c-means principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Incorporating biological knowledge for construction of fuzzy networks of gene associations

引用

APPLIED SOFT COMPUTING 2016年 42卷 144-155页

作者： Gomez-Vela, Francisco Barranco, Carlos D. Diaz-Diaz, Norberto Pablo de Olavide Univ Intelligent Data Anal DATAi Div Comp Sci ES-41013 Seville Spain

gene association networks have become one of the most important approaches to modelling of biological processes by means of gene expression data. According to the literature, co-expression-based methods are the main approaches to identification of gene association networks because such methods can identify gene expression patterns in a dataset and can determine relations among genes. These methods usually have two fundamental drawbacks. Firstly, they are dependent on quality of the input dataset for construction of reliable models because of the sensitivity to data noise. Secondly, these methods require that the user select a threshold to determine whether a relation is biologically relevant. Due to these shortcomings, such methods may ignore some relevant information. We present a novel fuzzy approach named FyNE (Fuzzy NEtworks) for modelling of gene association networks. FyNE has two fundamental features. Firstly, it can deal with data noise using a fuzzy-set-based protocol. Secondly, the proposed approach can incorporate prior biological knowledge into the modelling phase, through a fuzzy aggregation function. These features help to gain some insights into doubtful gene relations. The performance of FyNE was tested in four different experiments. Firstly, the improvement offered by FyNE over the results of a co-expression-based method in terms of identification of gene networks was demonstrated on different datasets from different organisms. Secondly, the results produced by FyNE showed its low sensitivity to noise data in a randomness experiment. Additionally, FyNE could infer gene networks with a biological structure in a topological analysis. Finally, the validity of our proposed method was confirmed by comparing its performance with that of some representative methods for identification of gene networks (C) 2016 Elsevier B.V. All rights reserved.

关键词： Computational intelligence gene network Fuzzy logic Biological knowledge gene expression data analysis

来源：评论

学校读者我要写书评

暂无评论

Ameliorative missing value imputation for robust biological knowledge inference

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2008年第4期41卷 499-514页

作者： Sehgal, Muhammad Shoaib B. Gondal, Iqbal Dooley, Laurence S. Coppel, Ross Monash Univ Fac Informat Technol Churchill Vic 3842 Australia Monash Univ Dept Microbiol Clayton Vic 3800 Australia Victorian Bioinformat Consortium Wellington Vic 3800 Australia

gene expression data is widely used in various post genomic analyses. The data is often probed using microarrays due to their ability to simultaneously measure the expressions of thousands of genes. The expression data, however, contains significant numbers of missing values, which can impact on subsequent biological analysis. To minimize the impact of these missing values, several imputation algorithms including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component analysis (BPCA), Least Square Impute (LSImpute), Local Least Square Impute (LLSImpute), and K-Nearest Neighbour (KNN) have been proposed. These algorithms, however, exploit either only the global or local correlation structure of the data, which normally can lead to higher estimation errors. This paper presents an Ameliorative Missing Value Imputation (AMVI) technique which has ability to exploit global/local and positive/negative correlations in a given dataset by automatic selection of the optimal number of predictor genes k using a wrapper non-parametric method based on Monte Carlo simulations. The AMVI technique has CMVE strategy at its core because CMVE has demonstrated improved performance compared to both low variance methods like BPCA, LLSImpute, and high variance methods such as KNN and ZeroImpute, as CMVE exploits positive/negative correlations. The performance of AMVI is compared with CMVE, BPCA, LLSImpute, and KNN by randomly removing between 1% and 15% missing values in eight different ovarian, breast cancer and yeast datasets. Together with the standard NRMS error metric, the True Positive (TP) rate of the significant genes selection, biological significance of the selected genes and the statistical significance test results are presented to investigate the impact of missing values on subsequent biological analysis. The enhanced performance of AMVI was demonstrated by its lower NRMS error, improved TP rate, biological significance of the selected genes and statistical sig

关键词： imputation microarrays gene expression data analysis missing value estimation non-negative least square optimization and significant gene selection

来源：评论

学校读者我要写书评

暂无评论

Towards knowledge-based gene expression data mining

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2007年第6期40卷 787-802页

作者： Bellazzi, Riccardo Zupan, Blaz Univ Ljubljana Fac Comp & Informat Sci SI-1000 Ljubljana Slovenia Univ Pavia Dipartimento Informat & Sistemist I-27100 Pavia Italy Baylor Coll Med Dept Mol & Human Genet Houston TX 77030 USA

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks. (C) 2007 Elsevier Inc. All rights reserved.

关键词： gene expression data analysis data mining knowledge-based data mining gene association classification gene networks

来源：评论

学校读者我要写书评

暂无评论

Methods of forward feature selection based on the aggregation of classifiers generated by single attribute

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2011年第7期41卷 435-441页

作者： Luo, Linkai Ye, Lingjun Luo, Meixiang Huang, Dengfeng Peng, Hong Yang, Fan Xiamen Univ Dept Automat Xiamen 361005 Peoples R China

Compared to backward feature selection (BFS) method in gene expression data analysis, forward feature selection (FFS) method can obtain an expected feature subset with less iteration. However, the number of FFS method is considerably less than that of BFS method. More efficient FFS methods need to be developed. In this paper, two FFS methods based on the pruning of the classifier ensembles generated by single attribute are proposed for gene selection. The main contributions are as follows: (1) a new loss function, p-insensitive loss function, is proposed to overcome the disadvantage of the margin Euclidean distance loss function in the pruning of classifier ensembles;(2) two FFS methods based on the margin Euclidean distance loss function and the p-insensitive loss function, named as FFS-ACSA1 and FFS-ACSA2 respectively, are proposed;(3) the comparison experiments on four gene expression datasets show that FFS-ACSA2 obtains the best results among three FFS methods (i.e. signal-to-noise ratio (SNR), FFS-ACSA1 and FFS-ACSA2), and is competitive to the famous support vector machine-based recursive feature elimination (SVM-RFE), while FFS-ACSA1 is unstable. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Forward feature selection gene expression data analysis Aggregation of classifiers SNR SVM-RFE

来源：评论

学校读者我要写书评

暂无评论

Diverse accurate feature selection for microarray cancer diagnosis

引用

INTELLIGENT data analysis 2013年第4期17卷 697-716页

作者： Hatami, Nima Chira, Camelia Univ Calif San Diego Imaging Data Evaluat & Anal Ctr La Jolla CA 92093 USA Univ Babes Bolyai Dept Comp Sci R-3400 Cluj Napoca Romania

gene expression microarray data provides simultaneous activity measurement of thousands of features facilitating a potential effective and reliable cancer diagnosis. An important and challenging task in microarray analysis refers to selecting the most relevant and significant genes for data (cancer) classification. A random subspace ensemble based method is proposed to address feature selection in gene expression cancer diagnosis. The introduced Diverse Accurate Feature Selection method relies on multiple individual classifiers built based on random feature subspaces. Each feature is assigned a score computed based on the pairwise diversity among individual classifiers and the ratio between individual and ensemble accuracies. This triggers the creation of a ranked list of features for which a final classifier is applied with an increased performance using minimum possible number of genes. Experimental results focus on the problem of gene expression cancer diagnosis based on microarray datasets publicly available. Numerical results show that the proposed method is competitive with related models from literature.

关键词： Random subspace ensembles multiple classifier systems multivariate feature selection gene expression data analysis pairwise diversity

来源：评论

学校读者我要写书评

暂无评论

Shifting-and-Scaling Correlation Based Biclustering Algorithm

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014年第6期11卷 1239-1252页

作者： Ahmed, Hasin Afzal Mahanta, Priyakshi Bhattacharyya, Dhruba Kumar Kalita, Jugal Kumar Tezpur Univ Dept Comp Sci & Engn Tezpur Assam India Univ Colorado Dept Comp Sci Colorado Springs CO 80907 USA

The existence of various types of correlations among the expressions of a group of biologically significant genes poses challenges in developing effective methods of gene expression data analysis. The initial focus of computational biologists was to work with only absolute and shifting correlations. However, researchers have found that the ability to handle shifting-and-scaling correlation enables them to extract more biologically relevant and interesting patterns from gene microarray data. In this paper, we introduce an effective shifting-and-scaling correlation measure named Shifting and Scaling Similarity (SSSim), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named Intensive Correlation Search (ICS) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression data set. The technique performs satisfactorily with a number of benchmarked gene expression data sets when evaluated in terms of functional categories in gene Ontology database.

关键词： Similarity measure biclustering gene expression data analysis shifting-and-scaling correlation

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：