检索结果-内蒙古大学图书馆

A variational Bayesian framework for group feature selection

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2013年第6期4卷 609-619页

作者： Subrahmanya, Niranjan Shin, Yung C. ExxonMobil Res & Engn Co Annandale NJ USA Purdue Univ W Lafayette IN 47907 USA

In many machine learning and pattern analysis applications, grouping of features during model development and the selection of a small number of relevant groups can be useful to improve the interpretability of the learned parameters. Although this problem has been receiving a significant amount of attention lately, most of the approaches require the manual tuning of one or more hyper-parameters. In order to overcome this drawback, this work presents a novel hierarchical Bayesian formulation of a generalized linear model and estimates the posterior distribution of the parameters and hyper-parameters of the model within a completely Bayesian paradigm based on variational inference. All the required computations are analytically tractable. The performance and applicability of the proposed framework is demonstrated on synthetic and real world examples.

关键词： Feature group selection Hierarchical bayes microarray data analysis Sensor selection Variational bayes Wavelength selection

来源：评论

学校读者我要写书评

暂无评论

Filtering non-balanced data using an evolutionary approach

引用

LOGIC JOURNAL OF THE IGPL 2023年第2期31卷 271-286页

作者： Carballido, Jessica A. Ponzoni, Ignacio Cecchini, Rocio L. Univ Nacl Sur UNS CONICET Inst Comp Sci & EngnDept Comp Sci & Engn San Andres 800 RA-8000 Bahia Blanca Buenos Aires Argentina

Matrices that cannot be handled using conventional clustering, regression or classification methods are often found in every big data research area. In particular, datasets with thousands or millions of rows and less than a hundred columns regularly appear in biological so-called omic problems. The effectiveness of conventional data analysis approaches is hampered by this matrix structure, which necessitates some means of reduction. An evolutionary method called PreCLAS is presented in this article. Its main objective is to find a submatrix with fewer rows that exhibits some group structure. Three stages of experiments were performed. First, a benchmark dataset was used to assess the correct functionality of the method for clustering purposes. Then, a microarray gene expression data matrix was used to analyze the method's performance in a simple classification scenario, where differential expression was carried out. Finally, several classification methods were compared in terms of classification accuracy using an RNA-seq gene expression dataset. Experiments showed that the new evolutionary technique significantly reduces the number of rows in the matrix and intelligently performs unsupervised row selection, improving classification and clustering methods.

关键词： Clustering tendency data reduction techniques evolutionary algorithm classification strategies unsupervised data filtering microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

Sparse Manifold Clustering and Embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2013年第11期43卷 1863-1869页

作者： Garcia-Gomez, Juan M. Gomez-Sanchis, Juan Escandell-Montero, Pablo Fuster-Garcia, Elies Soria-Olivas, Emilio Univ Politecn Valencia Biomed Informat Grp IBIME ITACA Valencia 46022 Spain Univ Valencia Dept Elect Engn Intelligent Data Anal Lab E-46100 Valencia Spain Hosp La Fe IIS Grp Invest Biomed Imagen GIBI230 Valencia Spain

Sparse Manifold Clustering and Embedding (SMCE) algorithm has been recently proposed for simultaneous clustering and dimensionality reduction of data on nonlinear manifolds using sparse representation techniques. In this work, SMCE algorithm is applied to the differential discrimination of Glioblastoma and Meningioma Tumors by means of their Gene Expression Profiles. Our purpose was to evaluate the robustness of this nonlinear manifold to classify gene expression profiles, characterized by the high-dimensionality of their representations and the low discrimination power of most of the genes. For this objective, we used SMCE to reduce the dimensionality of a preprocessed dataset of 35 single-labeling cDNA microarrays with 11500 original clones. Afterwards, supervised and unsupervised methodologies were applied to obtain the classification model: the former was based on linear discriminant analysis, the later on clustering using the SMCE embedding data. The results obtained using both approaches showed that all (100%) the samples could be correctly classified and the results of all repetitions but one formed a compatible cluster of predictive labels. Finally, the embedding dimensionality of the dataset extracted by SMCE revealed large discrimination margins between both classes. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Manifolds Automatic classification microarray data analysis Medical applications Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

TotalPLS: Local Dimension Reduction for Multicategory microarray data

引用

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2014年第1期44卷 125-138页

作者： You, Wenjie Yang, Zijiang Yuan, Mingshun Ji, Guoli Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China York Univ Sch Informat Technol Toronto ON M3J 1P3 Canada Xiamen Univ Innovat Ctr Cell Biol Xiamen 361102 Peoples R China

Dimension reduction is an important topic in data mining, which is widely used in the areas of genetics, medicine, and bioinformatics. We propose a new local dimension reduction algorithm TotalPLS that operates in a unified partial least squares (PLS) framework and implement an information fusion of PLSbased feature selection and feature extraction. This paper focuses on extracting the potential structure hidden in high-dimensional multicategory microarray data, and interpreting and understanding the results provided by the potential structure information. First, we propose using PLS-based recursive feature elimination (PLSRFE) in multicategory problems. Then, we perform feature importance analysis based on PLSRFE for high-dimensional microarray data to determine the information feature (biomarkers) subset, which relates to the studied tumor subtypes problem. Finally, PLS-based supervised feature extraction is conducted on the selected specific genes subset to extract comprehensive features that best reflect the nature of classification to have a discriminating ability. The proposed algorithm is compared with several state-of-the-art methods using multiple high-dimensional multicategory microarray datasets. Our comparison is performed in terms of recognition accuracy, relevance, and redundancy. Experimental results show that the algorithm proposed by us can improve the recognition rate and computational efficiency. Furthermore, mining potential structure information improves the interpretability and understandability of recognition results. The proposed algorithm can be effectively applied tomicroarray data analysis for the discovery of gene coexpression and coregulation.

关键词： Dimension reduction feature extraction feature selection microarray data analysis partial least squares (PLS)

来源：评论

学校读者我要写书评

暂无评论

Sparse optimal scoring for multiclass cancer diagnosis and biomarker detection using microarray data

引用

COMPUTATIONAL BIOLOGY AND CHEMISTRY 2008年第6期32卷 417-425页

作者： Leng, Chenlei Natl Univ Singapore Dept Stat & Appl Probabil Singapore 117546 Singapore

Gene expression data sets hold the promise to provide cancer diagnosis on the molecular level. However, using all the gene profiles for diagnosis may be suboptimal. Detection of the molecular signatures not only reduces the number of genes needed for discrimination purposes, but may elucidate the roles they play in the biological processes. Therefore, a central part of diagnosis is to detect a small set of tumor biomarkers which can be used for accurate multiclass cancer classification. This task calls for effective multiclass classifiers with built-in biomarker selection mechanism. We propose the sparse optimal scoring (SOS) method for multiclass cancer characterization. SOS is a simple prototype classifier based on linear discriminant analysis, in which predictive biomarkers can be automatically determined together with accurate classification. Thus, SOS differentiates itself from many other commonly used classifiers, where gene preselection must be applied before classification. We obtain satisfactory performance while applying SOS to several public data sets. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： microarray data analysis Biomarker detection Multiclass classification

来源：评论

学校读者我要写书评

暂无评论

MapReduce based parallel gene selection method

引用

APPLIED INTELLIGENCE 2015年第2期42卷 147-156页

作者： Islam, A. K. M. Tauhidul Jeong, Byeong-Soo Bari, A. T. M. Golam Lim, Chae-Gyun Jeon, Seok-Hee Kyung Hee Univ Dept Comp Engn Seoul South Korea

microarray data analysis has been widely used for extracting relevant biological information from thousands of genes simultaneously expressed in a specific cell. Although many genes are expressed in a sample tissue, most of these are irrelevant or insignificant for clinical diagnosis or disease classification because of missing values and noises. Thus, finding a small, closely related gene set to accurately classify disease cells is an important research problem. At the same time, scalable gene selection methods are required for microarray data analysis due to rapidly increasing volume of microarray data. In this paper, we propose a scalable parallel gene selection method using the M a p R e u d c e programming model. The proposed method utilizes the kNN classifier algorithm for evaluating classification accuracy and uses four real and three synthetic datasets for experiments. Experimental results show that the proposed method can offer good scalability on large data with increasing number of nodes and it can also provide higher classification accuracy rather than using whole gene set for classification.

关键词： microarray data analysis Gene selection MapReduce

来源：评论

学校读者我要写书评

暂无评论

Hybrid ant lion mutated ant colony optimizer technique for Leukemia prediction using microarray gene data

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2965-2973页

作者： Santhakumar, D. Logeswari, S. CK Coll Engn & Technol Comp Sci Engn Cuddalore Tamil Nadu India Bannari Amman Inst Technol Comp Sci Engn Satyamangalam Tamil Nadu India

The classification of cancers is one of the most vital functions of microarray data analysis. The classification of the gene expression profile is treated as a NP-Hard problem since it is a very demanding job. Compared to the individual search utilized by conventional algorithms, the population search utilized by Evolutionary Algorithm (EA) is visibly more beneficial. In feasible search areas, EA algorithms also are more likely to detect various optimums instantly. Evolutionary techniques which are inspired by nature perform exceptionally well and are extensively used for microarray data analysis. Ant Colony Optimization (ACO) is a distinct intelligent optimization algorithm based on iterative optimization which uses ideas like evolution and group. ACO algorithm was developed by studying how ants identify paths while food foraging. Ant Lion Optimization (ALO) algorithm is proposed and employed as muted selection process and the ant lions to hunt process is simulated. A hybrid ant lion mutated ant colony optimizer technique is proposed in this work.

关键词： microarray data analysis Ant Colony Optimization (ACO) Ant Lion Optimization (ALO) algorithm A hybrid ant lion mutated ant colony optimizer technique

来源：评论

学校读者我要写书评

暂无评论

Comparison of Feature Selection Methods for Cross-Laboratory microarray analysis

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013年第3期10卷 593-604页

作者： Liu, Hsi-Che Peng, Pei-Chen Hsieh, Tzung-Chien Yeh, Ting-Chi Lin, Chih-Jen Chen, Chien-Yu Hou, Jen-Yin Shih, Lee-Yung Liang, Der-Cherng Mackay Mem Hosp Mackay Med Coll New Taipei Taiwan Mackay Mem Hosp Div Pediat Hematol Oncol New Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 10764 Taiwan Natl Taiwan Univ Dept Bioind Mech Engn Taipei 10764 Taiwan Chang Gung Mem Hosp Dept Internal Med Div Hematol Oncol Taipei 10591 Taiwan Chang Gung Univ Sch Med Tao Yuan Taiwan

The amount of gene expression data of microarray has grown exponentially. To apply them for extensiVe studies, integrated analysis of cross-laboratory (cross-lab) data'becomes a trend, and thus, choosing an appropriate feature selection method is an essential issue. This paper focuses on,feature selection for Affymetrix.(Affy) microarray studies across differentlabs. We investigate four feature selection methods: t-test, significance analysis of microarrays (SAM), rank products'(RP), and random forest (RF)..The four methods are applied'to acute lymphoblastic leukemia, acute myeloid leukemia, breast cancer, and lung cancer Affy datawhich consist of three cross-lab data sets each. We utilize a rank-based normalization method to reduce thetias from cross-lab-data sets. Training on one data set or two combined data sets to test the remaining data set(s) are both' considered, Balanced accuracy is used for prediction evaluation. This study provides comprehensive comparisons of the four feature selection methods in cross-lab microarray analysis. Results show, that SAM has the best classification performance. RF also getS high classification accuracy, but it is not as stable as SAM. The most naive method is t-test, but its performance is the worst among the four methods. In thiS study, we further discuss the influence from the number of training samples, the number of selected genes, and the issue of unbalanced data sets.

关键词： microarray data analysis feature selection cancer cross-laboratory experiment

来源：评论

学校读者我要写书评

暂无评论

Gene expression and protein-protein interaction data for identification of colon cancer related genes using f-information measures

引用

NATURAL COMPUTING 2016年第3期15卷 449-463页

作者： Paul, Sushmita Maji, Pradipta Univ Erlangen Nurnberg Dept Dermatol Lab Syst Tumor Immunol Erlangen Germany Indian Stat Inst Biomed Imaging & Bioinformat Lab Machine Intelligence Unit Kolkata 700108 India

One of the most important and challenging problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new computational method to identify disease genes. It judiciously integrates the information of gene expression profiles and shortest path analysis of protein-protein interaction networks. While the -information based maximum relevance-maximum significance framework is used to select differentially expressed genes as disease genes using gene expression profiles, the functional protein association network is used to study the mechanism of diseases. An important finding is that some -information measures are shown to be effective for selecting relevant and significant genes from microarray data. Extensive experimental study on colorectal cancer establishes the fact that the genes identified by the integrated method have more colorectal cancer genes than the genes identified from the gene expression profiles alone, irrespective of any gene selection algorithm. Also, these genes have greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. The enrichment analysis of the obtained genes reveals to be associated with some of the important KEGG pathways. All these results indicate that the integrated method is quite promising and may become a useful tool for identifying disease genes.

关键词： microarray data analysis Gene selection f-Information Classification Protein-protein interaction network

来源：评论

学校读者我要写书评

暂无评论

Gene selection for enhanced classification on microarray data using a weighted k-NN based algorithm

引用

INTELLIGENT data analysis 2019年第1期23卷 241-253页

作者： Ventura-Molina, Elias Alarcon-Paredes, Antonio Aldape-Perez, Mario Yanez-Marquez, Cornelio Adolfo Alonso, Gustavo Inst Politecn Nacl Ctr Invest Computac Av Juan de Dios Batiz Ciudad De Mexico 07738 Mexico Univ Autonoma Guerrero Fac Ingn Av Lazaro Cardenas S-NCiudad Univ Zona Sur Chilpancingo Guerrero 39087 Mexico Inst Politecn Nacl Ctr Innovac & Desarrollo Tecnol Computo Av Juan de Dios Batiz Ciudad De Mexico 07700 Mexico

Feature selection is a common solution to microarray analysis. Previous approaches either select features based on classical statistical tests that can be tuned up with a classifier, or using regularization penalties incorporated in the cost function. Here we propose to use a feature ranking and weighting scheme instead, which combines statistical techniques with a weighted k-NN classifier using a modified forward selection procedure. We demonstrate that classification accuracy of our proposal outperforms existing methods on a range of public microarray gene expression datasets. The proposed method is also compared to state-of-the-art feature selection algorithms by means of the Friedman test. Although a bunch of feature selection techniques has been used for genomic data, the experimental results show the classification superiority of our method on most of the present gene expression datasets.

关键词： Computational genomics microarray data analysis feature selection feature ranking feature weighting k-nearest neighbors

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：