检索结果-内蒙古大学图书馆

Fourth International Conference on Emerging Applications of Information Technology

作者： Mandal, Sudip Saha, Goutam Pal, Rajat Kumar GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9781479942725

Suitable analysis of microarray dataset can unlock the mystery of the origin of many dreaded disease like cancer which can subsequently be investigated for its rectification, resulting into search for drug design. A critical challenge of the post-genomic era is to find out the cancer causing genes that induce changes in gene expression profiles in the microarray dataset. Various algorithms based on SVM, data Mining Techniques, Information theory based investigations, Clustering Techniques etc. were used by previous researchers. In this paper, Rough Set Theory and Bayesian Network based techniques have been applied for the same purpose. Rough Set has been used to isolate genes from microarray dataset responsible for cervical cancer. Bayesian approach has been used for extracting the Gene Regulating Network using the isolated genes. The same has been repeated for a normal healthy person. By superimposing these two networks, it is possible to find out the distinct cellular pathway for development of cancer from the departure of directed edges of the two networks. The results obtained in this work are quite satisfactory.

关键词： Cancer diagnosis microarray data Rough set Rule reduction Gene regulatory network Bayesian network

来源：评论

学校读者我要写书评

暂无评论

Random KNN 14

Random KNN

引用

14th IEEE International Conference on data Mining (IEEE ICDM)

作者： Li, Shengqiao Harner, E. James Adjeroh, Donald A. West Virginia Univ Dept Stat Morgantown WV 26506 USA West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781479942749

We present Random KNN, a novel generalization of traditional nearest-neighbor modeling. Random KNN consists of an ensemble of base k-nearest neighbor classifiers, each constructed from a random subset of the input variables. We study the properties of the proposed Random KNN. Using various datasets, we perform an empirical analysis of Random KNN performance and compare it with recently proposed methods for high-dimensional datasets. It is shown that Random KNN provides significant advantages in both the computational requirement and classification performance.

关键词： Classification Feature Selection High Dimensional data KNN microarray data Random KNN

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Algorithm Based on New Crossover for the Biclustering of Gene Expression data

Evolutionary Algorithm Based on New Crossover for the Biclus...

引用

9th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB)

作者： Maatouk, Ons Ayadi, Wassim Bouziri, Hend Duval, Beatrice Univ Tunis ISG Tunis LARODEC Lab Tunis Tunisia Univ Angers LERIA F-49045 Angers France Univ Tunis LaTICE Lab ESSTT Tunis Tunisia

ISBN: (纸本)9783319091921;9783319091914

microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed through biclustering method to determinate groups of genes presenting a similar behavior under specific groups of conditions. This paper proposes a new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data. This proposed crossover method ensures the creation of new biclusters with better quality. To evaluate its performance, an experimental study was done on real microarray datasets. These experimentations show that our algorithm extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure.

关键词： Biclustering Evolutionary algorithm Crossover method microarray data data mining

来源：评论

学校读者我要写书评

暂无评论

Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method

引用

Osong Public Health and Research Perspectives 2014年第6期5卷 324-332页

作者： Farhadian, Maryam Mahjub, Hossein Poorolajal, Jalal Moghimbeigi, Abbas Mansoorizadeh, Muharram Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Research Center for Health Sciences and Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Modeling of Noncommunicable Diseases Research Center Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Modeling of Noncommunicable Disease Research Center Department of Biostatistics and Epidemiology School of Public Health Hamadan University of Medical Sciences Hamadan Iran Department of Computer Engineering Faculty of Engineering Bu-Ali Sina University Hamadan Iran

Objectives: Classification of breast cancer patients into different risk classes is very important in clinical applications. It is estimated that the advent of high-dimensional gene expression data could improve patient classification. In this study, a new method for transforming the high-dimensional gene expression data in a low-dimensional space based on wavelet transform (WT) is presented. Methods: The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a predictive support vector machine (SVM) model was built upon the reduced dimensional space. In addition, the proposed method was compared with the supervised principal component analysis (PCA). Results: The performance of supervised wavelet and supervised PCA based on selected genes were better than the signature genes identified in the other studies. Furthermore, the supervised wavelet method generally performed better than the supervised PCA for predicting the 5-year survival status of patients with breast cancer based on microarray data. In addition, the proposed method had a relatively acceptable performance compared with the other studies. Conclusion: The results suggest the possibility of developing a new tool using wavelets for the dimension reduction of microarray data sets in the classification framework. © 2014.

关键词： Breast cancer microarray data Supervised wavelet Support vector machine

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

Ensemble component selection for improving ICA based microarray data prediction models

引用

PATTERN RECOGNITION 2009年第7期42卷 1274-1283页

作者： Liu, Kun-Hong Li, Bo Zhang, Jun Du, Ji-Xiang Xiamen Univ Sch Software Xiamen 361005 Fujian Peoples R China Wuhan Univ Sci & Techol Sch Comp Sci Technol Wuhan 430081 Hubei Peoples R China Anhui Univ Sch Elect Sci & Technol Hefei Anhui Peoples R China Huaqiao Univ Dept Comp Sci & Technol Quanzhou 362021 Fujian Peoples R China

Independent component analysis (ICA) has been widely used to tackle the microarray dataset classification problem, but there still exists an unsolved problem that the independent component (IC) sets may not be reproducible after different ICA transformations. Inspired by the idea of ensemble feature selection, we design an ICA based ensemble learning system to fully utilize the difference among different IC sets. In this system, some IC sets are generated by different ICA transformations firstly. A multi-objective genetic algorithm (MOGA) is designed to select different biologically significant IC subsets from these IC sets, which are then applied to build base classifiers. Three schemes are used to fuse these base classifiers. The first fusion scheme is to combine all individuals in the final generation of the MOCA. In addition, in the evolution, we design a global-recording technique to record the best IC subsets of each IC set in a global-recording list. Then the IC subsets in the list are deployed to build base classifier so as to implement the second fusion scheme. Furthermore, by pruning about half of less accurate base classifiers obtained by the second scheme, a compact and more accurate ensemble system is built, which is regarded as the third fusion scheme. Three microarray datasets are used to test the ensemble systems, and the corresponding results demonstrate that these ensemble schemes can further improve the performance of the ICA based classification model, and the third fusion scheme leads to the most accurate ensemble system with the smallest ensemble size. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： microarray data Independent component analysis (ICA) Multiple classifier system (MCS) Ensemble component selection Multi-objective genetic algorithm (MOGA) Global-recording technique

来源：评论

学校读者我要写书评

暂无评论

A sequential feature extraction approach for naive bayes classification of microarray data

引用

EXPERT SYSTEMS WITH APPLICATIONS 2009年第6期36卷 9919-9923页

作者： Fan, Liwei Poh, Kim-Leng Zhou, Peng Natl Univ Singapore Dept Ind & Syst Engn Singapore 119260 Singapore Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China

Accurate classification of microarray data plays a vital role in cancer prediction and diagnosis. Previous Studies have demonstrated the usefulness of naive Bayes classifier in solving various classification problems. In microarray data analysis, however, the conditional independence assumption embedded in the classifier itself and the characteristics of microarray data, e.g. the extremely high dimensionality, may severely affect the classification performance of naive Bayes classifier. This paper presents a sequential feature extraction approach for naive Bayes classification of microarray data. The proposed approach consists of feature selection by stepwise regression and feature transformation by class-conditional independent component analysis. Experimental results on five microarray datasets demonstrate the effectiveness of the proposed approach in improving the performance of naive Bayes classifier in microarray data analysis. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： microarray data Naive Bayes Feature extraction Independent component analysis (ICA) Stepwise regression

来源：评论

学校读者我要写书评

暂无评论

Prominent feature selection of microarray data

引用

Progress in Natural Science:Materials International 2009年第10期19卷 1365-1371页

作者： Yihui Liu School of Computer Science and Information Technology,Shandong Institute of Light Industry,Jinan 250353,China [a]School of Computer Science and Information Technology Shandong Institute of Light Industry Jinan 250353 China

For wavelet transform,a set of orthogonal wavelet basis aims to detect the localized changing features contained in microarray *** this research,we investigate the performance of the selected wavelet features based on wavelet detail coefficients at the second level and the third *** genetic algorithm is performed to optimize wavelet detail coefficients to select the best discriminant *** are carried out on four microarray datasets to evaluate the performance of *** results prove that wavelet features optimized from detail coefficients efficiently characterize the differences between normal tissues and cancer tissues.

关键词： microarray data Wavelet detail coefficients Feature extraction Feature selection

来源：评论

学校读者我要写书评

暂无评论

Selecting informative genes from microarray data by using hybrid methods for cancer classification

引用

ARTIFICIAL LIFE AND ROBOTICS 2009年第2期13卷 414-417页

作者： Mohamad, Mohd Saberi Omatu, Sigeru Deris, Safaai Misman, Muhammad Faiz Yoshioka, Michifumi Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai Johore Malaysia

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.

关键词： Cancer classification Genetic algorithm Gene selection Hybrid method microarray data

来源：评论

学校读者我要写书评

暂无评论

Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014年第2期11卷 347-360页

作者： GaneshKumar, Pugalendhi Rani, Chellasamy Devaraj, Durairaj Victoire, T. Aruldoss Albert Anna Univ Dept Informat Technol Reg Ctr Coimbatore Tamil Nadu India Govt Coll Engn Dept Comp Sci & Engn Salem India Kalasalingam Univ Dept Elect & Elect Engn Krishnankoil India Anna Univ Dept Elect & Elect Engn Reg Ctr Coimbatore Tamil Nadu India

Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.

关键词： microarray data fuzzy expert system ant colony optimization artificial bee colony mutual information

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：