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A novel aggregate gene selection method for microarray data classification

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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microarray data Classification Based on Neighbourhood Components Analysis Projection Method 6

Microarray Data Classification Based on Neighbourhood Compon...

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IEEE 6th International Conference on Big data Analytics (ICBDA)

作者： Zhang, Chuanlei Liu, Lixin Zhang, Shanwen Huang, Chengliang Tianjin Univ Sci & Technol Sch Artificial Intelligence Tianjin 300222 Peoples R China Xijing Univ Sch Informat Engn Xian 710123 Peoples R China Ryerson Univ Ted Rogers Sch Informat Technol Management Toronto ON M5B 2K3 Canada

ISBN: (纸本)9781665415132

microarray data can contribute to precise classification of tumors. However, it is difficult to solve the problems caused by the curse of dimensionality and small sample size of the microarray data. Neighbourhood components analysis (NCA) is a method which can maximize a stochastic variant of the leave-one-out KNN score on the training data set and can effectively learn linear projection matrices for dimensionality reduction in classification. Based on NCA, a discriminant projection method, called NCADP, is proposed to solve the two problems in tumor classification task. Firstly, the project matrix is constructed by using the classification probability. Secondly, the high-dimensionality microarray data are projected into a lower dimensional space. Finally, the K-nearest neighbourhood classifier is employed to classify the tumors in the low-subspace. The proposed NCADP is non-parametric, without making assumptions about the shape of the class distributions or the boundaries between them, and it achieved higher correct classification rate than traditional ones in the experiments over two public microarray datasets, which verified that the proposed method is effective and feasible for tumor classification.

关键词： microarray data tumor classification neighbourhood components analysis (NCA) discriminant analysis

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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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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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Finding best algorithmic components for clustering microarray data

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KNOWLEDGE AND INFORMATION SYSTEMS 2013年第1期35卷 111-130页

作者： Vukicevic, Milan Kirchner, Kathrin Delibasic, Boris Jovanovic, Milos Ruhland, Johannes Suknovic, Milija Univ Belgrade Fac Org Sci Belgrade Serbia Univ Jena Fac Econ & Business Adm Jena Germany

The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms.

关键词： Clustering microarray data Component-based algorithms Bioinformatics

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Prediction of NSCLC recurrence from microarray data with GEP

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IET SYSTEMS BIOLOGY 2017年第3期11卷 77-86页

作者： Al-Anni, Russul Hou, Jingyu Abdu-aljabar, Rana Dhia'a Xiang, Yong Deakin Univ Sch Informat Technol Geelong Vic Australia Al Nahrain Univ Informat Engn Coll Baghdad Iraq

Lung cancer is one of the deadliest diseases in the world. Non-small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.

关键词： lung cancer lab-on-a-chip genetics patient diagnosis NSCLC recurrence prediction microarray data GE programming nonsmall cell lung cancer cancer recurrence histopathology data microarray gene expression prognostic genes

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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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Variable selection for Fisher linear discriminant analysis using the modified sequential backward selection algorithm for the microarray data

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APPLIED MATHEMATICS AND COMPUTATION 2014年 238卷 132-140页

作者： Peng, Hong-Yi Jiang, Chun-Fu Fang, Xiang Liu, Jin-Shan South China Agr Univ Coll Sci Guangzhou 510642 Guangdong Peoples R China Shenzhen Univ Coll Math & Computat Sci Shenzhen 518060 Peoples R China South China Agr Univ Coll Food Sci Guangzhou 510642 Guangdong Peoples R China

One of the major challenges is small sample size as compared to large features number for microarray data. Variable selection is an important step for improving diagnostics of cancer or the classification according to the phenotypes via gene expression data. In this study, we propose a modified sequential backward selection (SBS) algorithm to deal with the case where the covariance matrix is singular. Then we propose a variable selection algorithm based on the weighted Mahalanobis distance and modified SBS methods. Furthermore, based on the proposed variable selection algorithm, a Fisher linear discriminant method is proposed to improve the accuracy of tumor classification through simultaneously taking into account genes' joint discriminatory power. To validate the efficiency, we apply the proposed discriminant method to two different DNA microarray data sets for experiment investigation. The empirical results show that our method for tumor classification can obtain better classification effectiveness than Markov random field method and independent variable group analysis I methods, which demonstrates that the proposed variable selection method can obtain more correct and informative gene subset if taking into account the joint discriminatory power of genes for tumor classification. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Variable selection Fisher linear discriminant analysis Modified SBS Weighted Mahalanobis distance microarray data

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The classification of cancer stage microarray data

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012年第3期108卷 1070-1077页

作者： Chen, Chi-Kan Natl Chung Hsing Univ Dept Appl Math Taichung Taiwan

Correctly diagnosing the cancer stage is most important for selecting an appropriate cancer treatment option for a patient. Recent advances in microarray technology allow the cancer stage to be predicted using gene expression patterns. The cancer stage is in ordinal scale. In this paper, we employ strict ordinal regressions including cumulative logit model in traditional statistics with data dimensionality reduction, and distribution free approaches of large margin rank boundaries implemented by the support vector machine, as well as an ensemble ranking scheme to model the cancer stage using gene expression microarray data. Predictive genes included in Models are selected by univariate feature ranking, and recursive feature elimination. We perform cross-validation experiments to assess and compare classification accuracies of ordinal and non-ordinal algorithms on five cancer stage microarray datasets. We conclude that a strict ordinal classifier trained by a validated approach can predict the cancer stage more accurately than traditional non-ordinal classifiers without considering the order of cancer stages. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

关键词： Classification Ordinal response Cancer stage microarray data

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Investigation of self-organizing oscillator networks for use in clustering microarray data

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2008年第1期7卷 65-79页

作者： Salem, Sameh A. Jack, Lindsay B. Nandi, Asoke K. Univ Liverpool Dept Elect Engn & Elect Signal Proc & Commun Grp Liverpool L69 3GJ Merseyside England

The self-organizing oscillator network (SOON) is a comparatively new clustering algorithm that does not require the knowledge of the number of clusters. The SOON is distance based, and its clustering behavior is different to density-based algorithms in a number of ways. This paper examines the effect of adjusting the control parameters of the SOON with four different datasets;the first is a (communications) modulation dataset representing one modulation scheme under a variety of noise conditions. This allows the assessment of the behavior of the algorithm with data varying between highly separable and nonseparable cases. The main thrust of this paper is to evaluate its efficacy in biological datasets. The second is taken from microarray experiments on the cell cycle of yeast, while the third and the fourth represent two microarray cancer datasets, i.e., the lymphoma and the liver cancer datasets. The paper demonstrates that the SOON is a viable tool to analyze these problems, and can add many useful insights to the biological data that may not always be available using other clustering methods.

关键词： cluster validation data clustering gene expression microarray data unsupervised classification

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