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Performance analysis of clustering techniques over microarray data: A case study

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2018年 493卷 162-176页

作者： Dash, Rasmita Misra, Bijan Bihari Siksha O Anusandhan Univ Inst Tech Educ & Res Dept Comp Sc & Informat Technol Khandagiri Sq Bhubaneswar 751030 Odisha India Silicon Inst Technol Dept Comp Sc & Engn Bhubaneswar 751024 Odisha India

Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test. (C) 2017 Elsevier B.V. All rights reserved.

关键词： microarray data Feature selection Cluster analysis Particle swarm optimization Statistical test

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A novel aggregate gene selection method for microarray data classification

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PATTERN RECOGNITION LETTERS 2015年 60-61卷 16-23页

作者： Thanh Nguyen Khosravi, Abbas Creighton, Douglas Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3216 Australia

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Gene selection Analytic hierarchy process Classification Gene expression profiles microarray data

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Low-precision feature selection on microarray data: an information theoretic approach

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第5期60卷 1333-1345页

作者： Moran-Fernandez, Laura Bolon-Canedo, Veronica Alonso-Betanzos, Amparo Univ A Coruna CITIC La Coruna Spain

The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the capacity of using machine learning models for pattern recognition, and even making decisions, with the added advantage of diminishing network congestion by allowing computations near to the data sources. The main restriction is the low computation capacity of these devices. Thus, machine learning algorithms capable of maintaining accuracy while using mechanisms that exploit certain characteristics, such as low-precision versions, are needed. In this paper, low-precision mutual information-based feature selection algorithms are employed over DNA microarray datasets, showing that 16-bit and some times even 8-bit representations of these algorithms can be used without significant variations in the final classification results achieved.

关键词： microarray data Low precision Feature selection Mutual information Classification Edge computing Internet of Things

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Exploring correlations in gene expression microarray data for maximum predictive-minimum redundancy biomarker selection and classification

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第10期43卷 1437-1443页

作者： Arevalillo, Jorge M. Navarro, Hilario Univ Nacl Educ Distancia Dept Stat Operat Res & Numer Anal E-28040 Madrid Spain

An important issue in the analysis of gene expression microarray data is concerned with the extraction of valuable genetic interactions from high dimensional data sets containing gene expression levels collected for a small sample of assays. Past and ongoing research efforts have been focused on biomarker selection for phenotype classification. Usually, many genes convey useless information for classifying the outcome and should be removed from the analysis;on the other hand, some of them may be highly correlated, which reveals the presence of redundant expressed information. In this paper we propose a method for the selection of highly predictive genes having a low redundancy in their expression levels. The predictive accuracy of the selection is assessed by means of Classification and Regression Trees (CART) models which enable assessment of the performance of the selected genes for classifying the outcome variable and will also uncover complex genetic interactions. The method is illustrated throughout the paper using a public domain colon cancer gene expression data set. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： microarray data Gene expression Biomarker selection Classification and prediction Redundancy Classification and regression tree

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Large-scale integration of cancer microarray data identifies a robust common cancer signature

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BMC BIOINFORMATICS 2007年第1期8卷 1-13页

作者： Xu, Lei Geman, Donald Winslow, Raimond L. Johns Hopkins Univ Inst Computat Med Baltimore MD 21218 USA Johns Hopkins Univ Ctr Cardiovasc Bioinformat & Modeling Baltimore MD 21218 USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA

Background: There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-ased diagnostic tests to determine the presence of cancer. Results: In this study, we collect and integrate similar to 1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the Top-Scoring Pair of Groups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups;those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data. Conclusion: By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.

关键词： microarray data microarray Platform Independent Test data Prostate Cancer data Cancer microarray data

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Optimization of dynamic bi-clustering based on improved genetic algorithm for microarray data

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PATTERN ANALYSIS AND APPLICATIONS 2024年第3期27卷 1-22页

作者： Ram, Pintu Kumar Kuila, Pratyay Natl Inst Technol Dept Comp Sci & Engn Ravangla 737139 Sikkim India

Due to the nature of microarray data, the analysis of genes/features for disease diagnosis is a challenging task. Generally, the data comes in the form of a 2D matrix, where the row represents the genes and the column indicates the various conditions. Bi-clustering is an emerging technique that can efficiently reveal patterns of genes. It can perform simultaneously with a subset of genes and conditions. Inspired by this, dynamic bi-clustering based on an improved genetic algorithm (GA) is proposed. The chromosomes are efficiently designed. In addition, the fitness function is derived by considering multiple conflicting objectives to measure the quality of a cluster. A novel mutation is designed by the correlation technique. The crossover and mutation rates are dynamically changed. The obtained outcomes of the proposed approach are compared with the various existing approaches, such as traditional GA, the dynamic dame parallel GA, the evolutionary local search algorithm, bi-phase evolutionary searching, and the evolutionary bi-clustering algorithm. Further, statistical tests such as the analysis of variance and Friedman test are executed to show the significance of the proposed model. A biological analysis is also performed.

关键词： Bi-cluster GA Correlation ANOVA microarray data

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Improving the genetic bee colony optimization algorithm for efficient gene selection in microarray data

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2018年第4期7卷 399-410页

作者： Angulo, Adrian Pino Shin, Kilho Velazquez-Rodriguez, Camilo Univ Hyogo Grad Sch Appl Informat Kobe Hyogo 6512197 Japan Univ Holguin Grp Proc Datos Biomed Holguin Cuba

Feature selection is a very critical component in the workflow of biomedical data mining applications. In particular, there is a need for feature selection methods that can find complex relationships among genes, yet computationally efficient. Within the scope of microarray data analysis, the genetic bee colony (Gbc) algorithm is one of the best feature selection algorithms, which leverages the combination between genetic and ant colony optimization algorithms to search for the optimal solution. In this paper, we analyse in depth the fundamentals lying behind the Gbc and propose some improvements in both efficiency and accuracy, so that researchers can even take more advantage of this excellent method. By (i) replacing the filtering phase of Gbc with a more efficient technique, (ii) improving the population generation in the artificial colony algorithm used in Gbc, and (iii) improving the exploitation method in Gbc, our experiments in microarray data sets reveal that our new method Gbc+ is not only significantly more accurate, but also around ten times faster on average than the original

关键词： Feature selection Gene selection microarray data Machine learning

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microarray data Classification Using Feature Selection and Regularized Methods with Sampling Methods 2nd

Microarray Data Classification Using Feature Selection and R...

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2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Jyothi, Saddi Reddy, Y. Sowmya Lavanya, K. Lakireddy Bali Reddy Coll Engn Mylavaram India

ISBN: (纸本)9789811925412;9789811925405

In recent studies of medical field especially, it is essential to assess the expression levels of genes using the microarray technology. Most of the medical diseases like breast cancer, lung cancer, and recent corona are estimated using the gene expressions. The study in this paper focused on performing both classification and feature selection on different microarray data. The gene expression data is high dimensional and extraction of optimal genes in microarray data is challenging task. The feature selection methods Recursive Feature Elimination (REE), Relief, LASSO (Least Absolute Shrinkage And Selection Operator) and Ridge were initially applied to extract optimal genes in microarray data. Later, applied a good number of multi classification methods which includes K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Multilayer Perceptron Networks (MLP), Random Forest (RF) and Logistic Regression (LR). But the combination of mentioned feature selection and classifications required high computation. However, resampling method (i.e., SMOIB = Synthetic Minority Oversampling Technique) prior to the feature selection which enhances the microarray data analysis in classification respectively. The resampling method, with combination of RFE and LASSO feature selection using SVM and LR classification outperforms compared to other methods.

关键词： Gene expression K-Nearest Neighbors LASSO Logistic Regression microarray data Multilayer Perceptron Networks Random Forest Recursive Feature Elimination Ridge Support vector machines

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Active learning for microarray data

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2008年第1期47卷 85-96页

作者： Vogiatzis, D. Tsapatsoulis, N. Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus Univ Peloponnese Dept Telecommun Sci & Technol Tripoli 22100 Greece

In supervised learning it is assumed that it is straightforward to obtain labeled data. However, in reality labeled data can be scarce or expensive to obtain. Active learning (AL) is a way to deal with the above problem by asking for the labels of the most "informative" data points. We propose an AL method based on a metric of classification confidence computed on a feature subset of the original feature space which pertains especially to the large number of dimensions (i.e. examined genes) of microarray experiments. DNA microarray expression experiments permit the systematic study of the correlation of the expression of thousands of genes. Feature selection is critical in the algorithm because it enables faster and more robust retraining of the classifier. The approach that is followed for feature selection is a combination of a variance measure and a genetic algorithm. We have applied the proposed method on DNA microarray data sets with encouraging results. In particular we studied data sets concerning: small round blue cell tumours (four types), Leukemia (two types), lung cancer (two types) and prostate cancer (healthy, unhealthy) (c) 2007 Elsevier Inc. All rights reserved.

关键词： active learning microarray data classification confidence feature selection genetic algorithms

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Permutation test for incomplete paired data with application to cDNA microarray data

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COMPUTATIONAL STATISTICS & data ANALYSIS 2012年第3期56卷 510-521页

作者： Yu, Donghyeon Lim, Johan Liang, Feng Kim, Kyunga Kim, Byung Soo Jang, Woncheol Univ Georgia Dept Epidemiol & Biostat Athens GA 30602 USA Seoul Natl Univ Dept Stat Seoul South Korea Univ Illinois Dept Stat Urbana IL USA Sookmyung Womens Univ Dept Stat Seoul South Korea Yonsei Univ Dept Appl Stat Seoul 120749 South Korea

A paired data set is common in microarray experiments, where the data are often incompletely observed for some pairs due to various technical reasons. In microarray paired data sets, it is of main interest to detect differentially expressed genes, which are usually identified by testing the equality of means of expressions within a pair. While much attention has been paid to testing mean equality with incomplete paired data in previous literature, the existing methods commonly assume the normality of data or rely on the large sample theory. In this paper, we propose a new test based on permutations, which is free from the normality assumption and large sample theory. We consider permutation statistics with linear mixtures of paired and unpaired samples as test statistics, and propose a procedure to find the optimal mixture that minimizes the conditional variances of the test statistics, given the observations. Simulations are conducted for numerical power comparisons between the proposed permutation tests and other existing methods. We apply the proposed method to find differentially expressed genes for a colorectal cancer study. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Colorectal cancer Incomplete paired data microarray data Permutation test

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