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Classification of microarray cancer data using ensemble approach

NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2013年第3期2卷 159-173页

作者： Nagi, Sajid Bhattacharyya, Dhruba Kr St Edmunds Coll Dept Comp Sci Shillong 793001 Meghalaya India Tezpur Univ Dept Comp Sci & Engn Napaam 784028 Assam India

An ensemble of classifiers is created by combining predictions of multiple component classifiers for improving prediction performance. In this paper, we conduct experimental comparison of J48, NB, IBK on nine microarray cancer datasets and also analyze their performance with Bagging, Boosting and Stack Generalization. The experimental results show that all ensemble methods outperform the individual classification methods. We then present a method, referred to as SD-EnClass, for combining classifiers from different classification families into an ensemble, based on a simple estimation of each classifier's class performance. The experimental results show that the proposed model improves classification accuracy, in comparison to simply selecting the best classifier in the combination. In the second stage, we combine the results of our proposed method with the results of Boosting, Bagging and Stacking using the combining method proposed, to obtain results which are significantly better than using Boosting, Bagging or Stacking alone.

关键词： Bagging Boosting Cancer datasets Classifier Ensemble Meta-ensemble microarray data Stacking

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Merging microarray studies to identify a common gene expression signature to several structural heart diseases

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BIOdata MINING 2020年第1期13卷 8-8页

作者： Fajarda, Olga Duarte-Pereira, Sara Silva, Raquel M. Oliveira, Jose Luis Univ Aveiro IEETA DETI P-3810193 Aveiro Portugal Univ Aveiro Dept Med Sci P-3810193 Aveiro Portugal Univ Aveiro iBiMED Inst Biomed P-3810193 Aveiro Portugal Univ Catolica Portuguesa Fac Med Dent CIIS Ctr Invest Interdisciplinar Saude Campus Viseu P-3504505 Viseu Portugal

Background Heart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used. Results Nine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjustedp-value and fold change were determined and by combining a set of adjustedp-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed genes that has the best accuracy in classifying samples from patients with heart diseases and samples from patients with no heart condition, the random forest algorithm was used. A set of 62 differentially expressed genes having a classification accuracy of approximately 95% was identified. Conclusions We identified a gene expression signature common to different cardiac diseases and supported our findings by showing their involvement in the pathophysiology of the heart. The approach used in this study is suitable for the identification of gene expression signatures, and can be extended to different diseases.

关键词： Heart disease Random forest Gene expression signature microarray data

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Enhanced Cancer Subtyping via Pan-Transcriptomics data Fusion, Monte-Carlo Consensus Clustering, and Auto Classifier Creation 10

Enhanced Cancer Subtyping via Pan-Transcriptomics Data Fusio...

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10th International Conference on Computational Systems-Biology and Bioinformatics (CSBio)

作者： Linton-Reid, Kristofer Clifford, Harry Thompson, Joe Sneath Imperial Coll London London England Cambridge Canc Genom Cambridge England

ISBN: (纸本)9781450372152

Subtyping of tumor transcriptome expression profiles is a routine method used to distinguish tumor heterogeneity. Unsupervised clustering techniques are often combined with survival analysis to decipher the relationship between genes and the survival times of patients. However, the reproducibility of these subtyping based studies is poor. There are multiple reports which have conflicting subtype and gene-survival time relationship results. In this study, we introduce the issues underlying the lack of reproducibility in transcriptomic subtyping studies. This problem arises from the routine analysis of small cohorts (< 100 individuals) and use of biased traditional consensus clustering techniques. Our approach carefully combines multiple RNA-sequencing and microarray datasets, followed by subtyping via Monte-Carlo Consensus Clustering and creation of deep subtyping classifiers. This paper demonstrates an improved subtyping methodology by investigating pancreatic ductal adenocarcinoma. Importantly, our methodology identifies six biologically novel pancreatic ductal adenocarcinoma subtypes. Our approach also enables a degree of reproducibility, via our pancreatic ductal adenocarcinoma classifier PDACNet, which classical subtyping studies have failed to establish.

关键词： Consensus Clustering Cancer Subtyping Gene Expression Pancreatic Cancer RNA-Sequencing data microarray data

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A New F-Test Applicable to Large-Scale data

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Journal of Statistical Theory and Applications 2019年第4期18卷 439-449页

作者： Salehi, Mohsen Mohammadpour, Adel Mengersen, Kerrie Department of Statistics University of Qom Qom Iran Department of Statistics Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Science and Engineering Faculty Queensland University of Technology Brisbane Australia

In large-scale multiple testing, the permutation test based on making a null statistic has been widely employed in the literature. Because it enables us to use the null permuted samples and estimate the p-value more accurately. Some test statistics, which can be modified to a null statistic, have been proposed for two independent groups. In this paper, we propose a new statistic and corresponding F-test, which can be applied for three or more groups. The simulation results demonstrate that the proposed method has a smaller interquartile range (IQR) than the usual permutation method and estimates p-values more accurately than its rival. © 2019, The Authors. Published by Atlantis Press SARL.

关键词： F-test microarray data Null statistic Permutation test

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Population based study on arsenic induced blood samples employing hybrid metaheuristic optimization based ML approach

Population based study on arsenic induced blood samples empl...

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IEEE-Region-10 Symposium (TENSYMP)

作者： Dey, Anirban Das Sharma, Kaushik Sanyal, Tamalika Bhattacharjee, Pritha, Jr. Bhattacharjee, Pritha Univ Calcutta Dept Appl Phys Kolkata India Univ Calcutta Dept Environm Sci Kolkata India

ISBN: (纸本)9781728102979

Long term exposure to arsenic may lead to the development of multi-organ ailments with several non-dermatological, non-malignant outcomes and hence considered as a paradoxical human carcinogen, requires major attention with comprehensive clinical data analysis. An experimental investigation has been pursued to analyze microarray data set and classify biologically arsenic affected genes endorsed by different machine learning approaches. microarray data consists of a huge number of biologically insignificant genes. Thus it leads to an expensive computation and restrains the classification accuracy. A thorough and critical analysis has been performed to determine a small set of nonredundant genes to classify the arsenic disease. The analysis outcomes could be further put into action to interpret. the therapeutic measures to be taken care of the disease. An experimental investigation has been carried out to deal with this population-based learning problem with the objective of classifying a the small set of gene expressions of the disease for particular demography. The analysis has been accomplished with the implementation of a hybrid metaheuristic optimization based machine learning methods for classification of arsenic disease into various classes.

关键词： Arsenic disease microarray data metaheuristic optimization machine learning methods

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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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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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Enhanced cancer subtyping via pan-transcriptomics data fusion, Monte-Carlo consensus clustering, and auto classifier creation 19

Enhanced cancer subtyping via pan-transcriptomics data fusio...

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Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics

作者： Kristofer Linton-Reid Harry Clifford Joe Sneath Thompson Imperial College London South Kensington London United Kingdom Cambridge Cancer Genomics Cambridge United Kingdom

ISBN: (纸本)9781450372152

关键词： consensus clustering cancer subtyping RNA-sequencing data microarray data pancreatic cancer gene expression

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Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第16期39卷 12371-12377页

作者： Nahar, Jesmin Imam, Tasadduq Tickle, Kevin S. Ali, A. B. M. Shawkat Chen, Yi-Ping Phoebe Cent Queensland Univ Fac Arts Business Informat & Educ Rockhampton Qld 4702 Australia La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Vic 3086 Australia

The objective of this paper was to perform a comparative analysis of the computational intelligence algorithms to identify breast cancer in its early stages. Two types of data representations were considered: microarray based and medical imaging based. In contrast to previous researches, this research also considered the imbalanced nature of these data. It was observed that the SMO algorithm performed better for the majority of the test data, especially for microarray based data when accuracy was used as performance measure. Considering the imbalanced characteristic of the data, the Naive Bayes algorithm was seen to perform highly in terms of true positive rate (TPR). Regarding the influence of SMOTE, a well-known imbalanced data classification technique, it was observed that there was a notable performance improvement for J48, while the performance of SMO remained comparable for the majority of the datasets. Overall, the results indicated SMO as the most potential candidate for the microarray and image dataset considered in this research. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Breast cancer microarray data Image data Computational intelligence SMOTE SMO

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Clustering microarray data: Theoretical and Practical Issues

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2012年第16-17期41卷 3211-3232页

作者： Di Lascio, F. Marta L. Giannerini, Simone Univ Bologna Dept Stat Sci I-40126 Bologna Italy

The analysis of microarray data is a widespread functional genomics approach that allows for the monitoring of the expression of thousands of genes at once. The analysis of the great amount of data generated in a microarray experiment requires powerful statistical techniques. One of the first tasks of the analysis of microarray data is to cluster data into biologically meaningful groups according to their expression patterns. In this article, we discuss classical as well as recent clustering techniques for microarray data. We pay particular attention to both theoretical and practical issues and give some general indications that might be useful to practitioners.

关键词： Distance-based clustering Distance measures microarray data Model-based clustering

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