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Regularized logistic regression without a penalty term: An application to cancer classification with microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 5110-5118页

作者： Bielza, Concha Robles, Victor Larranaga, Pedro Tech Univ Madrid Dept Artificial Intelligence Madrid Spain Tech Univ Madrid Dept Comp Architecture & Technol Madrid Spain

Regularized logistic regression is a useful classification method for problems with few samples and a huge number of variables. This regression needs to determine the regularization term, which amounts to searching for the optimal penalty parameter and the norm of the regression coefficient vector. This paper presents a new regularized logistic regression method based on the evolution of the regression coefficients using estimation of distribution algorithms. The main novelty is that it avoids the determination of the regularization term. The chosen simulation method of new coefficients at each step of the evolutionary process guarantees their shrinkage as an intrinsic regularization. Experimental results comparing the behavior of the proposed method with Lasso and ridge logistic regression in three cancer classification problems with microarray data are shown. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Logistic regression Regularization Estimation of distribution algorithms Cancer classification microarray data

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Sequential patterns mining and gene sequence visualization to discover novelty from microarray data

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JOURNAL OF BIOMEDICAL INFORMATICS 2011年第5期44卷 760-774页

作者： Sallaberry, A. Pecheur, N. Bringay, S. Roche, M. Teisseire, M. Irstea UMR TETIS Maison Teledetect F-34093 Montpellier France Univ Montpellier 3 MIAp Dept F-34199 Montpellier 5 France Univ Montpellier 2 CNRS LIRMM F-34095 Montpellier 5 France INRIA Bordeaux Sud Ouest LaBRI F-33405 Talence France

data mining allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyze by end users. In this paper, we focus on sequential pattern mining and propose a new visualization system to help end users analyze the extracted knowledge and to highlight novelty according to databases of referenced biological documents. Our system is based on three visualization techniques: clouds, solar systems, and treemaps. We show that these techniques are very helpful for identifying associations and hierarchical relationships between patterns among related documents. Sequential patterns extracted from gene data using our system were successfully evaluated by two biology laboratories working on Alzheimer's disease and cancer. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Visualization data mining Bioinformatics Sequential patterns microarray data Gene data

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A SIMPLIFIED SWARM OPTIMIZATION FOR DISCOVERING THE CLASSIFICATION RULE USING microarray data OF BREAST CANCER

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2011年第5A期7卷 2235-2246页

作者： Yeh, Wei-Chang Chang, Wei-Wen Chiu, Chuan-Wei Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan

microarray data analysis is a major line of research in bioinformatics. A significant trend in bioinformatics is identifying genes or gene groups that differentiate diseased tissues. Classification is necessary to make microarray data useful for application in medicine, and in related research such as disease diagnosis. Classification models have been developed using statistical methods such as logistic and multi-normal regression for data mining. However, the complexities of real-world classification problems, such as those in the medical domain, are highly dimensional. General statistical methods are inadequate for these complex problems. This study proposes simplified swarm optimization (SSO), an efficient methodology for discovering breast cancer classification rules. The data set was derived from, the Stanford microarray database. The proposed approach enables simultaneous feature selection and pattern recognition. Experimental results indicate that SSO outperforms general data mining methods such as decision tree, neural network, support vector machine, etc. The proposed approach has potential applications in hospital decision-making and research such as predictive medicine.

关键词： microarray data Simplified swarm optimization Pattern recognition

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Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 4661-4667页

作者： Lee, Chien-Pang Lin, Wen-Shin Chen, Yuh-Min Kuo, Bo-Jein Natl Chung Hsing Univ Dept Agron Div Biometry Taichung 40227 Taiwan China Med Univ Sch Nursing Taichung 40402 Taiwan

Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey some of the assumptions of parametric statistical methods, or type I error may be over expanded. Therefore, our aim is to establish a gene selection method without assumption restriction to reduce the dimension of the data set. In our study, adaptive genetic algorithm/k-nearest neighbor (AGA/KNN) was used to evolve gene subsets. We find that AGA/KNN can reduce the dimension of the data set, and all test samples can be classified correctly. In addition, the accuracy of AGA/KNN is higher than that of GA/KNN, and it only takes half the CPU time of GA/KNN. After using the proposed method, biologists can identify the relevant genes efficiently from the sub-gene set and classify the test samples correctly. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Gene selection Sample classification Adaptive genetic algorithm k-Nearest neighbor microarray data

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Exploring the Feasibility of Next-Generation Sequencing and microarray data Meta-Analysis

Exploring the Feasibility of Next-Generation Sequencing and ...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Wu, Po-Yen Phan, John H. Wang, May D. Georgia Inst Technol Dept Elect & Comp Engn Atlanta GA 30332 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA

ISBN: (纸本)9781424441228

Emerging next-generation sequencing (NGS) technology potentially resolves many issues that prevent widespread clinical use of gene expression microarrays. However, the number of publicly available NGS datasets is still smaller than that of microarrays. This paper explores the possibilities for combining information from both microarray and NGS gene expression datasets for the discovery of differentially expressed genes (DEGs). We evaluate several existing methods in detecting DEGs using individual datasets as well as combined NGS and microarray datasets. Results indicate that analysis of combined NGS and microarray data is feasible, but successful detection of DEGs may depend on careful selection of algorithms as well as on data normalization and pre-processing.

关键词： Bioinformatics Biomedical engineering Cancer Computational modeling data models Gene expression Genomics NGS gene expression datasets biology computing data analysis data normalization differentially expressed genes genetics lab-on-a-chip microarray data

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The Design of Evolutionary Multiple Classifier System for the Classification of microarray data

The Design of Evolutionary Multiple Classifier System for th...

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8th International Symposium on Neural Networks

作者： Liu, Kun-Hong Wu, Qing-Qiang Wang, Mei-Hong Xiamen Univ Software Sch Xiamen 361005 Fujian Province Peoples R China

ISBN: (纸本)9783642211102

Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. In detail, we construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. Then we construct an ensemble system with the individuals in last generation in two ways: using the nondominated individuals: using all the individuals accompanied with a classifier selection process based on another GA. We test the two proposed ensemble methods based on two microarray data sets, and the experimental results show that these two methods are robust and can lead to promising results.

关键词： multiple classifier system genetic algorithm microarray data

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A Novel Approach to Select Important Genes from microarray data

A Novel Approach to Select Important Genes from Microarray D...

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23rd Chinese Control and Decision Conference

作者： Wang, Xianchang Zhang, Lishi Du, Junfu Dalian Ocean Univ Sch Sci Dalian 116023 Peoples R China

ISBN: (纸本)9781424487363

Feature subset selection is a well-known pattern recognition problem, which aims to reduce the number of features used in classification or recognition. This reduction is expected to improve the performance of classification algorithms in terms of speed, accuracy and simplicity. Most existing feature selection investigations are not suitable for microarray data, so this paper focuses on gene selection problem. The main contributions of this paper are that a new feature selection method A-score is introduced and constructed an improved fuzzy Bayesian classifier. We evaluate the performance of A-score using three well-known benchmark data sets: the iris data, the wine data, and the Wisconsin breast cancer data and two microarray data: ALL-AML Leukemia and colon cancer. In general, A-score can significantly reduce the number of genes, and perform better than T-score and C-score.

关键词： microarray data Feature selection Important genes A-score

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FEATURE DISCRETIZATION AND SELECTION IN microarray data

FEATURE DISCRETIZATION AND SELECTION IN MICROARRAY DATA

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International Conference on Knowledge Discovery and Information Retrieval

作者： Ferreira, Artur Figueiredo, Mario Inst Super Engn Lisboa Lisbon Portugal Inst Super Tecn Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9789898425799

Tumor and cancer detection from microarray data are important bioinformatics problems. These problems are quite challenging for machine learning methods, since microarray datasets typically have a very large number of features and small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. This paper proposes unsupervised feature discretization and selection methods suited for microarray data. The experimental results reported, conducted on public domain microarray datasets, show that the proposed discretization and selection techniques yield competitive and promising results with the best previous approaches. Moreover, the proposed methods efficiently handle multi-class microarray data.

关键词： Feature selection Feature discretization microarray data Tumor detection Cancer detection

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Numerical Methods for Genetic Regulatory Network Identification Based on a Variational Approach

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COMPUTATIONAL METHODS IN APPLIED MATHEMATICS 2016年第1期16卷 77-103页

作者： Feng, Xiaobing Yoon, Miun Univ Tennessee Dept Math Knoxville TN 37996 USA

This paper studies differential equation-based mathematical models and their numerical solutions for genetic regulatory network identification. The primary objectives are to design, analyze, and test a general variational framework and numerical methods for seeking its approximate solutions for reverse engineering genetic regulatory networks from microarray datasets. In the proposed variational framework, no structure assumption on the genetic network is presumed, instead, the network is solely determined by the microarray profile of the network components and is identified through a well chosen variational principle which minimizes an energy functional. The variational principle serves not only as a selection criterion to pick up the right solution of the underlying differential equation model but also provides an effective mathematical characterization of the small-world property of genetic regulatory networks which has been observed in lab experiments. Five specific models within the variational framework and efficient numerical methods and algorithms for computing their solutions are proposed and analyzed. Model validations using both synthetic network datasets and subnetwork datasets of Saccharomyces cerevisiae (yeast) and E. coli are performed on all five proposed variational models and a performance comparison versus some existing genetic regulatory network identification methods is also provided.

关键词： Genetic network identification differential equation modeling variational methods matrix norms singular value decomposition quasi-Newton methods microarray data model validation

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Hidden discriminative features extraction for supervised high-order time series modeling

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COMPUTERS IN BIOLOGY AND MEDICINE 2016年第0期78卷 81-90页

作者： Ngoc Anh Thi Nguyen Yang, Hyung-Jeong Kim, Sunhee Chonnam Natl Univ Dept Comp Sci Gwangju 500757 South Korea Univ Danang Univ Educ Fac Informat Technol Da Nang Vietnam Korea Univ Dept Brain & Cognit Engn Seoul 136713 South Korea

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative sub-spaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel x frequency bin x time frame and a microarray data that is modeled as gene x sample x time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix based algorithms and recent tensor-based, discriminant-decomposition approaches;this is especially the case considering the small number of samples that are used in practice. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Electroencephalogram (EEG) microarray data High-order time series Discriminant analysis Multi-way arrays Tucker decomposition Dimensionality reduction Seizure prediction

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