检索结果-内蒙古大学图书馆

Autoregressive-Model-Based Missing Value Estimation for DNA microarray Time Series data

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2009年第1期13卷 131-137页

作者： Choong, Miew Keen Charbit, Maurice Yan, Hong Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Ecole Natl Super Telecommun Bretagne Dept Signal & Image Proc F-75634 Paris France City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.

关键词： Autoregressive (AR) model microarray data analysis missing value estimation time series analysis

来源：评论

学校读者我要写书评

暂无评论

Direct integration of microarrays for selecting informative genes and phenotype classification

引用

INFORMATION SCIENCES 2008年第1期178卷 88-105页

作者： Yoon, Youngrai Lee, Jongchan Park, Sanghyun Bien, Sangjay Chung, Hyun Cheol Rha, Sun Young Yonsei Univ Dept Comp Sci Seoul 120749 South Korea Yonsei Univ Coll Med Canc Metastasis Res Ctr Dept Internal Med Seoul 120749 South Korea Gachon Univ Med & Sci Dept Informat Technol Inchon South Korea

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. (C) 2007 Elsevier Inc. All rights reserved.

关键词： data mining microarray data analysis microarray data integration microarray data classification Informative gene selection

来源：评论

学校读者我要写书评

暂无评论

Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification

引用

APPLIED SOFT COMPUTING 2018年 62卷 203-215页

作者： Jain, Indu Jain, Vinod Kumar Jain, Renu Jiwaji Univ Sch Math & Allied Sci Gwalior 474006 MP India PDPM Indian Inst Informat Technol Design & Mfg Dumna Airport RdPO Khamaria Jabalpur MP India

DNA microarray technology has emerged as a prospective tool for diagnosis of cancer and its classification. It provides better insights of many genetic mutations occurring within a cell associated with cancer. However, thousands of gene expressions measured for each biological sample using microarray pose a great challenge. Many statistical and machine learning methods have been applied to get most relevant genes prior to cancer classification. A two phase hybrid model for cancer classification is being proposed, integrating Correlation-based Feature Selection (CFS) with improved-Binary Particle Swarm Optimization (iBPSO). This model selects a low dimensional set of prognostic genes to classify biological samples of binary and multi class cancers using NaiveBayes classifier with stratified 10-fold cross-validation. The proposed iBPSO also controls the problem of early convergence to the local optimum of traditional BPSO. The proposed model has been evaluated on 11 benchmark microarray datasets of different cancer types. Experimental results are compared with seven other well known methods, and our model exhibited better results in terms of classification accuracy and the number of selected genes in most cases. In particular, it achieved up to 100% classification accuracy for seven out of eleven datasets with a very small sized prognostic gene subset (up to <1.5%) for all eleven datasets. (C) 2017 Elsevier B.V. All rights reserved.

关键词： microarray data analysis Cancer classification Improved Binary Particle Swarm Optimization (iBPSO) Hybrid model Gene selection Naive-Bayes

来源：评论

学校读者我要写书评

暂无评论

microarray data Classifier Consisting of k-Top-Scoring Rank-Comparison Decision Rules With a Variable Number of Genes

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2010年第2期40卷 216-226页

作者： Yoon, Youngmi Bien, Sangjay Park, Sanghyun Gachon Univ Med & Sci Dept Informat Technol Inchon 406799 South Korea Seoul Natl Univ Interdisciplinary Program Bioinformat Seoul 151742 South Korea Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for phenotype classification of many diseases. Our proposed phenotype classifier is an ensemble method with k-top-scoring decision rules. Each rule involves a number of genes, a rank comparison relation among them, and a class label. Current classifiers, which are also ensemble methods, consist of k-top-scoring decision rules. Some of these classifiers fix the number of genes in each rule as a triple or a pair. In this paper, we generalize the number of genes involved in each rule. The number of genes in each rule ranges from 2 to N, respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Our algorithm saves resources by combining shorter rules in order to build a longer rule. It converges rapidly toward its high-scoring rule list by implementing several heuristics. The parameter k is determined by applying leave-one-out cross validation to the training dataset.

关键词： data mining knowledge-based data mining microarray data analysis microarray data classification

来源：评论

学校读者我要写书评

暂无评论

Laplacian Linear Discriminant analysis Approach to Unsupervised Feature Selection

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2009年第4期6卷 605-614页

作者： Niijima, Satoshi Okuno, Yasushi Kyoto Univ Dept PharmacoInformat Ctr Integrat Educ Pharm Frontier Grad Sch Pharmaceut SciSakyo Ku Kyoto 6068501 Japan

Until recently, numerous feature selection techniques have been proposed and found wide applications in genomics and proteomics. For instance, feature/gene selection has proven to be useful for biomarker discovery from microarray and mass spectrometry data. While supervised feature selection has been explored extensively, there are only a few unsupervised methods that can be applied to exploratory data analysis. In this paper, we address the problem of unsupervised feature selection. First, we extend Laplacian linear discriminant analysis (LLDA) to unsupervised cases. Second, we propose a novel algorithm for computing LLDA, which is efficient in the case of high dimensionality and small sample size as in microarray data. Finally, an unsupervised feature selection method, called LLDA-based Recursive Feature Elimination (LLDA-RFE), is proposed. We apply LLDA-RFE to several public data sets of cancer microarrays and compare its performance with those of Laplacian score and SVD-entropy, two state-of-the-art unsupervised methods, and with that of Fisher score, a supervised filter method. Our results demonstrate that LLDA-RFE outperforms Laplacian score and shows favorable performance against SVD-entropy. It performs even better than Fisher score for some of the data sets, despite the fact that LLDA-RFE is fully unsupervised.

关键词： Unsupervised feature selection linear discriminant analysis graph Laplacian microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

Margin-based wrapper methods for gene identification using microarray

引用

NEUROCOMPUTING 2006年第16-18期69卷 2236-2243页

作者： Chen, Xue-Wen Univ Kansas Dept Elect Engn & Comp Sci Informat & Telecommun Technol Ctr Lawrence KS 66045 USA

The gene expression data obtained from microarrays have shown useful in cancer classification problems. DNA microarray data have extremely high dimensionality compared with the small number of available samples. An important step in microarray studies is to select a small number of features (genes) for cancer classifications. In this paper, we propose a novel feature selection algorithm to identify a small set of genes from microarray data for cancer classification. This new wrapper method uses margins to evaluate subsets of genes;bootstrap methods are applied in genetic search to alleviate small sample size problems and support vector machines with maximum margins are used for classifications. The results of experiments carried out show that the proposed method successfully identifies genes informative for cancer classification and provides very reliable cancer classification results. (c) 2005 Elsevier B.V. All rights reserved.

关键词： cancer classification feature selection genetic algorithms microarray data analysis support vector machines

来源：评论

学校读者我要写书评

暂无评论

Multi-step dimensionality reduction and semi-supervised graph-based tumor classification using gene expression data

引用

ARTIFICIAL INTELLIGENCE IN MEDICINE 2010年第3期50卷 181-191页

作者： Gui, Jie Wang, Shu-Lin Lei, Ying-Ke Chinese Acad Sci Intelligent Comp Lab Hefei Inst Intelligent Machines Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Inst Elect Engn Hefei 230037 Anhui Peoples R China

Objective Both supervised methods and unsupervised methods have been widely used to solve the tumor classification problem based on gene expression profiles This paper introduces a semi-supervised graph-based method for tumor classification Feature extraction plays a key role in tumor classification based on gene expression profiles and can greatly improve the performance of a classifier In this paper we propose a novel multi-step dimensionality reduction method for extracting tumor-related features Methods and materials First the Wilcoxon rank-sum test is used for gene selection Then gene ranking and discrete cosine transform are combined with principal component analysis for feature extraction Finally the performance is evaluated by semi-supervised learning algorithms Results To show the validity of the proposed method we apply it to classify four tumor datasets involving various human normal and tumor tissue samples The experimental results show that the proposed method is efficient and feasible Compared with other methods our method can achieve relatively higher prediction accuracy Particularly it is found that semi-supervised method is superior to support vector machines in classification performance Conclusions The proposed approach can effectively improve the performance of tumor classification based on gene expression profiles This work is a meaningful attempt to explore and apply multi-step dimensionality reduction and semi-supervised learning methods in the field of tumor classification Considering the high classification accuracy there should be much room for the application of multi-step dimensionality reduction and semi-supervised learning methods to perform tumor classification (C) 2010 Elsevier B V All rights reserved

关键词： Multi-step dimensionality reduction Gene ranking Discrete cosine transform Principal component analysis Semi-supervised learning microarray data analysis Tumor diagnosis

来源：评论

学校读者我要写书评

暂无评论

APPROXIMATION ALGORITHMS FOR BICLUSTERING PROBLEMS

引用

SIAM JOURNAL ON COMPUTING 2008年第4期38卷 1504-1518页

作者： Wang, Lusheng Lin, Yu Liu, Xiaowen City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

One of the main goals in the analysis of microarray data is to identify groups of genes and groups of experimental conditions (including environments, individuals, and tissues) that exhibit similar expression patterns. This is the so-called biclustering problem. In this paper, we consider two variations of the biclustering problem: the consensus submatrix problem and the bottleneck submatrix problem. The input of the problems contains an m x n matrix A and integers l and k. The consensus submatrix problem is to find an l x k submatrix with l < m and k < n and a consensus vector such that the sum of distances between the rows in the submatrix and the consensus vector is minimized. The bottleneck submatrix problem is to find an l x k submatrix with l < m and k < n, an integer d and a center vector such that the distance between every row in the submatrix and the vector is at most d and d is minimized. We show that both problems are NP-hard and give randomized approximation algorithms for special cases of the two problems. Using standard techniques, we can derandomize the algorithms to get polynomial time approximation schemes for the two problems. To the best of our knowledge, this is the first time that approximation algorithms with guaranteed ratios are presented for microarray data analysis.

关键词： approximation algorithms computational biology microarray data analysis genes

来源：评论

学校读者我要写书评

暂无评论

SEGS: Search for enriched gene sets in microarray data

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2008年第4期41卷 588-601页

作者： Trajkovski, Igor Lavrac, Nada Tolar, Jakub Jozef Stefan Inst Dept Knowledge Technol Ljubljana 1000 Slovenia Univ Nova Gorica Nova Gorica Slovenia Univ Minnesota Div Hematol Oncol & Blood & Marrow Transplantat Minneapolis MN 55455 USA

Gene Ontology (GO) terms are often used to interpret the results of microarray experiments. The most common approach is to perform Fisher's exact tests to find gene sets annotated by GO terms which are over-represented among the genes declared to be differentially expressed in the analysis of microarray data. Another way is to apply Gene Set Enrichment analysis (GSEA) that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. However, after correcting for multiple hypotheses testing, few (or no) GO terms may meet the threshold for statistical significance, because the relevant biological differences are small relative to the noise inherent to the microarray technology. In addition to the individual GO terms, we propose testing of gene sets constructed as intersections of GO terms, Kyoto Encyclopedia of Genes and Genomes Orthology (KO) terms, and gene sets constructed by using gene-gene interaction data obtained from the ENTREZ database. Our method finds gene sets that are significantly over-represented among differentially expressed genes which cannot be found by the standard enrichment testing methods applied on individual GO and KO terms, thus improving the enrichment analysis of microarray data. (C) 2007 Elsevier Inc. All rights reserved.

关键词： microarray data analysis ontology gene set enrichment

来源：评论

学校读者我要写书评

暂无评论

NFκB pathway analysis: An approach to analyze gene co-expression networks employing feedback cycles

引用

COMPUTATIONAL BIOLOGY AND CHEMISTRY 2018年 72卷 62-76页

作者： Dillenburg, Fabiane Cristine Zanotto-Filho, Alfeu Fonseca Moreira, Jose Claudio Ribeiro, Leila Carro, Luigi Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Fed Santa Catarina CCB Dept Farmacol Florianopolis SC Brazil Univ Fed Rio Grande do Sul ICBS Dept Bioquim Porto Alegre RS Brazil

The genes of the NF kappa B pathway are involved in the control of a plethora of biological processes ranking from inhibition of apoptosis to metastasis in cancer. It has been described that Gliobastoma multiforme (GBM) patients carry aberrant NF kappa B activation, but the molecular mechanisms are not completely understood. Here, we present a NF kappa B pathway analysis in tumor specimens of GBM compared to nonneoplasic brain tissues, based on the different kind of cycles found among genes of a gene co-expression network constructed using quantized data obtained from the microarrays. A cycle is a closed walk with all vertices distinct (except the first and last). Thanks to this way of finding relations among genes, a more robust interpretation of gene correlations is possible, because the cycles are associated with feedback mechanisms that are very common in biological networks. In GBM samples, we could conclude that the stoichiometric relationship between genes involved in NF kappa B pathway regulation is unbalanced. This can be measured and explained by the identification of a cycle. This conclusion helps to understand more about the biology of this type of tumor. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： NF kappa B microarray data analysis Gene co-expression networks Cycles Feedback mechanisms Gliobastoma multiforme

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：