检索结果-内蒙古大学图书馆

IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Guo, Shun Guo, Donghui Xiamen Univ Dept Elect Engn Fujian 361005 Peoples R China Xiamen Univ IC Design & IT Res Ctr Fujian Prov Fujian 361005 Peoples R China

ISBN: (纸本)9781467395984

In this paper, we propose a novel dimension reduction algorithm that implements an information fusion of Centroid-based feature selection and partial least squares (PLS) based feature extraction. This paper focuses on mining the potential information hidden in multiclass microarray data and interpreting the results provided by the potential information. Firstly, a centroid concept has been introduced to define the objective function of feature selection. In order to obtain the sparse solution, logistic regression with L1 regularization has been incorporated into the objective function. The Centroid-based feature selection is then proposed to solve the optimization problem. By using the One-Versus-All (OVA) techniques, the Centroid-based feature selection is extended to solve multiclass problems. Secondly, we perform feature important analysis on microarray data by Centroid-based feature selection to determine the information feature subset (biomarkers). Finally, PLS-based feature extraction is conducted on the selected feature subset to extract the features that best reflect the nature of classification. The proposed algorithm is compared with three state-of-the-art algorithms using eight multiclass microarray datasets. The experimental results demonstrate that the proposed algorithm performs effectively and is competitive. Furthermore, mining the potential information of the microarray dataset improves the interpretability of the results.

关键词： Dimension reduction Centroid L1 regularization partial least squares (PLS) microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

引用

4th International Workshop on Algorithms in Bioinformatics (WABI 2004)

作者： Ye, JP Li, T Xiong, T Janardan, R Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Florida Int Univ Sch Comp Sci Miami FL 33199 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds);that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear Discriminant analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called Uncorrelated Linear Discriminant analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the Generalized Singular Value Decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets.

关键词： microarray data analysis discriminant analysis generalized singular value decomposition classification

来源：评论

学校读者我要写书评

暂无评论

An improved Binary Particle Swarm Optimization (iBPSO) for Gene Selection and Cancer Classification using DNA microarrays

An improved Binary Particle Swarm Optimization (iBPSO) for G...

引用

Conference on Information and Communication Technology (CICT)

作者： Jain, Indu Jaint, Vinod Kumar Jain, Renu Jiwaji Univ Sch Math & Allied Sci SOMAAS Gwalior 474006 MP India PDPM IIITDM Comp Sci & Engn Discipline Jabalpur 482005 MP India

ISBN: (纸本)9781538682159

DNA microarrays enable the detection of genetic changes attributable to cancer by simultaneously analyzing the expression of thousands of genes. However, the identification of most relevant genes from thousands of gene expressions available in each biological sample, for cancer classification pose a great challenge. Although researchers have applied BPSO based wrapper approaches to get most relevant genes prior to cancer classification, these approaches didn't achieve good classification accuracy due to the premature convergence caused by local stagnation problem. This paper proposes an improved Binary Particle Swarm Optimization (iBPSO) to tackle these issues. The proposed iBPSO based wrapper is examined using Naive-Bayes (NB), k-Nearest Neighbor (kNN), and Support Vector Machines (SVM) classifiers with stratified 5-fold cross-validation. The proposed iBPSO exhibited its efficacy in terms of classification accuracy and the number of selected genes in comparison to standard BPSO on six benchmark cancer microarray datasets. Our proposed iBPSO also effectively escapes from local minima stagnation.

关键词： microarray data analysis cancer classification improved binary particle swarm optimization (iBPSO) gene selection

来源：评论

学校读者我要写书评

暂无评论

DPBC: Distance Based Possibilistic Biclustering With Application to Gene Expression analysis

DPBC: Distance Based Possibilistic Biclustering With Applica...

引用

3rd International Conference on Bioinformatics and Biomedical Engineering

作者： Mahfouz, Mohamed A. Ismail, Mohamed A. Univ Alexandria Fac Engn Dept Comp & Syst Engn Alexandria 21544 Egypt

ISBN: (纸本)9781424429011

Biclustering is a key step in analyzing gene expression data by identifying patterns where subset of genes are co-related based on a subset of conditions. This paper proposes a new distance based possibilistic biclustering algorithm (DPBC), in which the average distances between rows and between columns of the bicluster are minimized and at the same time the size of the bicluster is maximized by computing the zeros of the derivative of appropriate objective function. The proposed algorithm uses the possibilistic clustering paradigm similar to another existing possibilistic biclustering algorithm PBC. Whereas PBC is based on residue our approach is applicable to any accepted definition for distances between pairs of rows or columns. Experimental study on the human dataset and several artificial datasets having different noise levels shows that the DPBC algorithm can offer substantial improvements over the previously proposed algorithms.

关键词： Possibilistic biclustering fuzzy clustering biclustering bidimensional clustering microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

Simulations of simple artificial genetic networks reveal features in the use of Relevance Networks

引用

In Silico Biology 2005年第3期5卷 239-249页

作者： Lindlöf, Angelica Lubovac, Zelmina University of Skövde 541 28 Skövde Box 408 Sweden

Recent research on large scale microarray analysis has explored the use of Relevance Networks to find networks of genes that are associated to each other in gene expression data. In this work, we compare Relevance Networks with other types of clustering methods to test some of the stated advantages of this method. The dataset we used consists of artificial time series of Boolean gene expression values, with the aim of mimicking microarray data, generated from simple artificial genetic networks. By using this dataset, we could not confirm that Relevance Networks based on mutual information perform better than Relevance Networks based on Pearson correlation, partitional clustering or hierarchical clustering, since the results from all methods were very similar. However, all three methods successfully revealed the subsets of co-expressed genes, which is a valuable step in identifying co-regulation. © 2005 - IOS Press and Bioinformation Systems e.V. and the authors. All rights reserved.

关键词： Clustering microarray data analysis Regulatory networks Relevance Networks Simulation

来源：评论

学校读者我要写书评

暂无评论

Numerical deconvolution of cDNA microarray signal - Simulation study

引用

APPLICATIONS OF BIOINFORMATICS IN CANCER DETECTION 2004年第1期1020卷 110-123页

作者： Rosenfeld, S Wang, T Kim, Y Milner, J NCI Biometry Res Grp Div Canc Prevent Dept Hlth & Human ServNIH Rockville MD 20892 USA USDA Phytonutrients Lab Beltsville MD 20705 USA NCI Div Canc Prevent Nutr Sci Res Grp Dept Hlth & Human ServNIH Rockville MD 20892 USA

A computational model for simulation of the cDNA microarray experiments has been created. The simulation allows one to foresee the statistical properties of replicated experiments without actually performing them. We introduce a new concept, the so-called bio-weight, which allows for reconciliation between conflicting meanings of biological and statistical significance in microarray experiments. It is shown that, for a small sample size, the bio-weight is a more powerful criterion of the presence of a signal in microarray data as compared to the standard approach based on t test. Joint simulation of microarray and quantitative PCR data shows that the genes recovered by using the bio-weight have better chances to be confirmed by PCR than those obtained by the t test technique. We also employ extreme value considerations to derive plausible cutoff levels for hypothesis testing.

关键词： microarray data analysis numerical simulation polymerase chain reaction (PCR) replicated experiment

来源：评论

学校读者我要写书评

暂无评论

Analyzing the large numbers of variables in biomedical and satellite imagery /

引用

2011年

作者： Good Phillip I.

来源：内蒙古大学图书馆图书评论

学校读者我要写书评

暂无评论

A model-free greedy gene selection for microarray sample class prediction

A model-free greedy gene selection for microarray sample cla...

引用

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

作者： Shi, Yi Cai, Zhipeng Xu, Lizhe Ren, Wei Goebel, Randy Lin, Guohui Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada Chinese Acad Sci Acad Math & Syst Sci Beijing 100080 Peoples R China Univ New Orleans Res Inst Children New Orleans LA 70118 USA

ISBN: (纸本)1424406234

microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most significantly differentially expressed genes under different categories of conditions, is both computationally and biologically interesting, and has become a central research focus in all studies that use gene expression microarray technology. Despite many existing efforts, better gene selection methods that can effectively identify biologically significant biomarkers, yet computationally efficient, are still in need. In this paper, a model-free greedy (MFG) gene selection method is proposed, which implements several intuitive heuristics but doesn't assume any statistical distribution on the expression data. The experimental results on three real microarray datasets showed that the MFG method combined with a Support Vector Machine (SVM) classifier or a k-Nearest Neighbor (KNN) classifier is efficient and robust in identifying discriminatory genes.

关键词： microarray data analysis sample class prediction discriminatory gene gene selection greedy

来源：评论

学校读者我要写书评

暂无评论

Predicting microRNA Expression from Sequence 6

引用

6th European Conference of the International-Federation-for-Medical-and-Biological-Engineering (MBEC)

作者： Ogul, Hasan Tuncer, M. Emre Baskent Univ Dept Comp Engn TR-06490 Ankara Turkey

ISBN: (纸本)9783319111278

Given the promoter sequence of a microRNA, we attempt to predict its expression using a regression model learnt from the expression levels of other microRNAs obtained through a microarray experiment. To our knowledge, this is the first study that evaluates the predictability of microRNA expression from sequence. The promising results encourage the use of the system as a supporting means for microarray missing data imputation or completing old experiments with new explorations.

关键词： Regression analysis Relevance Vector Machines microarray data analysis microRNA regulation. missing data imputation promoter elements

来源：评论

学校读者我要写书评

暂无评论

IDENTIFICATION OF GENES CONSISTENTLY CO-EXPRESSED IN MULTIPLE microarray dataSETS BY A GENOME-WIDE BI-COPAM APPROACH

IDENTIFICATION OF GENES CONSISTENTLY CO-EXPRESSED IN MULTIPL...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abu-Jamous, Basel Fa, Rui Roberts, David J. Nandi, Asoke K. Brunel Univ Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England Univ Oxford Natl Hlth Service Blood & Transport Oxford England Univ Jyvaskyla Dep Mat Informat Technmol Jyvaskyla Finland

ISBN: (纸本)9781479903566

Many methods have been proposed to identify informative subsets of genes in microarray studies in order to focus the research. For instance, the recently proposed binarization of consensus partition matrices (Bi-CoPaM) method has, amongst its various features, the ability to generate tight clusters of genes while leaving many genes unassigned from all clusters. We propose exploiting this particular feature by applying the Bi-CoPaM over genome-wide microarray data from multiple datasets to generate more clusters than required. Then, these clusters are tightened so that most of their genes are left unassigned from all clusters, and most of the clusters are left totally empty. The tightened clusters, which are still not empty, include those genes that are consistently co-expressed in multiple datasets when examined by various clustering methods. An example of this is demonstrated in this paper for cyclic and acyclic genes as well as for genes that are highly expressed and that are not. Thus, the results of our proposed approach cannot be reproduced by other methods of genes' periodicity identification or by other methods of clustering.

关键词： Bi-CoPaM co-expressed genes genome-wide clustering microarray data analysis

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：