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Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第1期42卷 612-627页

作者： Kar, Subhajit Das Sharma, Kaushik Maitra, Madhubanti Future Inst Engn & Management Dept Elect Engn Kolkata India Univ Calcutta Dept Appl Phys Kolkata India Jadavpur Univ Dept Elect Engn Kolkata India

These days, microarray gene expression data are playing an essential role in cancer classifications. However, due to the availability of small number of effective samples compared to the large number of genes in microarray data, many computational methods have failed to identify a small subset of important genes. Therefore, it is a challenging task to identify small number of disease-specific significant genes related for precise diagnosis of cancer sub classes. In this paper, particle swarm optimization (PSO) method along with adaptive K-nearest neighborhood (KNN) based gene selection technique are proposed to distinguish a small subset of useful genes that are sufficient for the desired classification purpose. A proper value of K would help to form the appropriate numbers of neighborhood to be explored and hence to classify the dataset accurately. Thus, a heuristic for selecting the optimal values of K efficiently, guided by the classification accuracy is also proposed. This proposed technique of finding minimum possible meaningful set of genes is applied on three benchmark microarray datasets, namely the small round blue cell tumor (SRBCT) data, the acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) data and the mixed-lineage leukemia (MLL) data. Results demonstrate the usefulness of the proposed method in terms of classification accuracy on blind test samples, number of informative genes and computing time. Further, the usefulness and universal characteristics of the identified genes are reconfirmed by using different classifiers, such as support vector machine (SVM). (C) 2014 Elsevier Ltd. All rights reserved.

关键词： microarray data SRBCT data ALL_AML data MLL data Particle swarm optimization (PSO) Adaptive K-nearest neighborhood (KNN) Support vector machine (SVM)

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Markov Blanket: Efficient Strategy For Feature Subset Selection Method For High Dimensional microarray Cancer datasets

Markov Blanket: Efficient Strategy For Feature Subset Select...

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Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Passi, Kalpdrum Nour, Abdala Jain, Chakresh Kumar Laurentian Univ Dept Math & Comp Sci Sudbury ON Canada Jaypee Inst Informat Technol Dept Biotechnol Noida India

ISBN: (纸本)9781509030507

In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection is by using Bayesian network based on Markov blanket. We present and compare the performance of the different approaches of features (genes) subset selection methods based on Wrapper and Markov Blanket models for the five-microarray cancer datasets. The first alternative depends on Memetic algorithms (MAs) for feature selection method. In the second alternative, we use MRMR (Minimum Redundant Maximum Relevant) for feature subset selection method hybridized by genetic search optimization techniques. We compare the performance of Markov blanket model with most common classification algorithms for those set of features. The results show that the performance measures of classification algorithms based on Markov Blanket model mostly offer better accuracy rates than other types of classical classification algorithms for the cancer microarray datasets.

关键词： microarray data feature subset selection Markov blanket Memetic algorithms Minimum Redundant Maximum Relevant

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Identification of Differentially Expressed Genes through a Meta-analysis Approach for Oral Cancer Classification 39

Identification of Differentially Expressed Genes through a M...

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39th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Kourou, Konstantina Papaloukas, Costas Fotiadis, Dimitrios I. Univ Ioannina Dept Mat Sci & Engn Unit Med Technol & Intelligent Informat Syst GR-45110 Ioannina Greece Fdn Res & Technol Hellas FORTH Iraklion Greece Univ Ioannina Dept Biol Applicat & Technol GR-45110 Ioannina Greece

ISBN: (纸本)9781509028092

We propose a meta-analysis scheme for identifying differentially expressed genes in Oral Squamous Cell Carcinoma (OSCC) from different microarray studies. We detect a subset of relevant features and further classify samples under two experimental conditions (i.e healthy and cancer samples) for better patient stratification. A well-established meta-analysis method is adopted and gene expression data sets are derived from a public functional genomics data repository. Our primary aim is the accurate identification of up-and down-regulated genes in order to extract valuable biological information concerning the changes in expression between healthy and cancer samples. According to our results and the extracted informative gene list, a high classification accuracy of healthy and OSCC tumors is achieved with as few genes as possible. Furthermore, the proposed scheme implies that the combination of datasets from different origins may reduce the estimated percentage of false predictions, while the power of gene identification and disease classification is increased.

关键词： differential expression disease classification informative genes meta-analysis microarray data oral cancer

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Goodness-of-Fit Test for Large Number of Small data Sets

Goodness-of-Fit Test for Large Number of Small Data Sets

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作者： Lee, Hyuneui Texas A&M University

学位级别：Ph.D.

A goodness-of-fit (gof) problem, i.e., testing whether observed data come from a specific distribution is one of the important problems in statistics, and various tests for checking distributional assumptions have been suggested. Most tests are for one data set with a large enough sample sizes. However, this research focuses on the gof problem when there are a large number of small data sets. In other words, we assume that the number of data sets p increases to infinity and the sample size of each small data set n is finite. In this dissertation, we will denote p and n as the number of data sets and the sample sizes of each data sets, respectively. Since the primary interest of this dissertation is testing whether every small data set comes from a known parametric family of distributions with different parameters, it is important to choose a gof test invariant to parameters of unknown distribution. Hence, as a basic approach, we suggest applying empirical distribution function (edf) based gof tests to every small data set and then combining P-values to obtain a single test. Two P-value combining methods, moment based tests and smoothing based tests, are suggested and their pros and cons are discussed. Especially, the two moment based tests, Edgington's method and Fisher's method, are compared with respect to Pitman efficiency and asymptotic power. We also find conditions that guarantee that the asymptotic null distribution of moment based tests based on empirical P-values is the same as that based on exact P-values. When the null is a location and scale family, there is no difficulty in applying the suggested test procedures. However, when the null is not a location and scale family, edf-based tests may depend on unknown parameters. To handle such a problem, we suggest using unconditional P-values and this requires an additional step of estimating the distribution of unknown parameters. Several issues related to estimating the distribution of unknown parameters and

关键词： Goodness-of-fit test microarray data Fisher’s method Edgington’s method Smoothing based tests Thesis

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A gene selection method for microarray data based on risk genes

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第11期38卷 14065-14071页

作者： Wong, Tzu-Tsung Chen, Ding-Qun Natl Cheng Kung Univ Inst Informat Management Tainan 701 Taiwan

Many gene selection methods have been proposed to select a subset of genes that can have a high prediction accuracy for cancer classification, and most set the same preference for all genes. However, many biological reports have pointed out that mutated or flawed genes, named as risk genes, can be one of the major causes of a specific disease. This study proposes a gene selection method based on the risk genes found in biological reports. The information provided by risk genes can reduce the time complexity for gene selection and increase the accuracy of cancer classification. This gene selection method is composed of two stages. Since all risk genes must be chosen, the first stage is to remove the genes that have similar expression levels or functions to risk genes. The next stage is to perform gene selection and gene replacement based on the results of a process that divides the remaining genes into clusters. Based on the test results from four microarray data sets, our gene selection method outperforms those proposed by previous studies, and genes that have the potential to be new risk genes are presented. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Cancer classification Clustering analysis Gene selection microarray data Risk genes

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Analysis of gene microarray data in a soft computing framework

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APPLIED SOFT COMPUTING 2011年第6期11卷 4152-4160页

作者： Maulik, Ujjwal Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India

In this article, the performance of three major components of soft computing viz., genetic algorithm, simulated annealing and differential evolution have been studied for developing fuzzy clustering of microarray gene expression data. microarray technology permits simultaneous monitoring of the expression levels of a large number of genes over different time points. Clustering is an important unsupervised analysis tool in this domain for finding groups of co-expressed genes. In this regard, the performance of the well-known fuzzy c-means algorithm is also studied in addition to the above mentioned soft computing-based approaches. Subsequently, support vector machine, a well-known technique for supervised learning, is utilized to improve the result of the clustering techniques. For this purpose, a fraction of the data points selected from different clusters based on their proximity to the respective centers, is used for training the support vector machine. The cluster assignments of the remaining points are thereafter determined using the trained classifier. Two publicly available benchmark microarray data sets have been used for demonstrating the effectiveness of the proposed approaches. Biological significance tests have been performed to show that the proposed method is able to produce biologically relevant clusters of co-expressed genes. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Biological significance test Differential evolution Fuzzy clustering Genetic algorithm microarray data Simulated annealing Soft computing

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Extraction of Informative Genes from Multiple microarray data Integrated by Rank-Based Approach

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2011年第4期E94D卷 841-854页

作者： Hong, Dongwan Yoon, Jeehee Lee, Jongkeun Park, Sanghyun Kim, Jongil Hallym Univ Dept Comp Engn Chunchon South Korea Seoul Natl Univ Coll Med Dept Biochem & Mol Biol Seoul 151 South Korea Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

By converting the expression values of each sample into the corresponding rank values, the rank-based approach enables the direct integration of multiple microarray data produced by different laboratories and/or different techniques. In this study, we verify through statistical and experimental methods that informative genes can be extracted from multiple microarray data integrated by the rank-based approach (briefly, integrated rank-based microarray data). First, after showing that a nonparametric technique can be used effectively as a scoring metric for rank-based microarray data, we prove that the scoring results from integrated rank-based microarray data are statistically significant. Next, through experimental comparisons, we show that the informative genes from integrated rank-based microarray data are statistically more significant than those of single-microarray data. In addition, by comparing the lists of informative genes extracted from experimental data, we show that the rankbased data integration method extracts more significant genes than the z-score-based normalization technique or the rank products technique. Public cancer microarray data were used for our experiments and the marker genes list from the CGAP database was used to compare the extracted genes. The GO database and the GSEA method were also used to analyze the functionalities of the extracted genes.

关键词： microarray data data integration informative gene significance test

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Integrated analysis of the heterogeneous microarray data

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BMC BIOINFORMATICS 2011年第Sup5期12卷 1-8页

作者： Yi, Sung Gon Park, Taesung Seoul Natl Univ Dept Stat Seoul South Korea Case Western Reserve Univ Dept Epidemiol & Biostat Cleveland OH 44106 USA

Background: As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. We propose the linear mixed effect model for the integrated analysis of the heterogeneous microarray data sets. Results: The proposed linear mixed effect model was illustrated using the data from 133 microarrays collected at three different hospitals. Though simulation studies, we compared the proposed linear mixed effect model approach with the meta-analysis and the ANOVA model approaches. The linear mixed effect model approach was shown to provide higher powers than the other approaches. Conclusions: The linear mixed effect model has advantages of allowing for various types of covariance structures over ANOVA model. Further, it can handle easily the correlated microarray data such as paired microarray data and repeated microarray data from the same subject.

关键词： microarray data Covariance Structure ANOVA Model Linear Mixed Effect Model Statistical Test Procedure

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Hierarchical mixture models for biclustering in microarray data

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STATISTICAL MODELLING 2011年第6期11卷 489-505页

作者： Martella, F. Alfo, M. Vichi, M. Univ Roma La Sapienza Dipartimento Sci Stat I-00185 Rome Italy

In the last few years, model-based clustering techniques have become widely used in the context of microarray data analysis. In this empirical context, a potential purpose for statistical approaches is the identification of clusters of genes that are co-expressed under subsets of experimental conditions. We discuss a hierarchical mixture model to combine advantages of allowing for dependence within gene clusters and for simultaneous clustering of genes and experimental conditions. Thanks to the adopted hierarchical structure, we may distinguish gene clusters from mixture components, where the latter may represent intra-cluster gene-specific extra-Gaussian departures. To cluster experimental conditions, instead, we suggest a suitable parameterization of component-specific means by using a binary row stochastic matrix representing condition membership. The performance of the proposed approach is discussed on both simulated and real datasets.

关键词： Hierarchical mixture model biclustering microarray data

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Wavelet selection for disease classification by DNA microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第1期38卷 990-995页

作者： Nanni, Loris Lumini, Alessandra Univ Bologna DEIS IEIIT CNR I-40136 Bologna Italy

The microarrays report the measures of the expression levels of tens of thousands of genes, this high dimensional feature vector contains also irrelevant information for accurate classification. Moreover, only few training samples are available, hence for avoiding the curse of dimensionality problem a feature reduction should be performed before the classification step. Here, we proposed a set of orthogonal wavelet detail coefficients of different wavelet mothers to extract the features from the microarray data. We propose to use a multi-classifiers where each classifier, a support vector machine, is trained using a different set of detail coefficients, the classifiers are combined by "sum rule". The detail coefficients set selection is performed by running Sequential Forward Floating Selection (SFFS). The goodness of the proposed method is validated using the area under the ROC curve as performance indicator, the experiments are carried out on four-datasets: Breast dataset;Ovarian dataset;Lung dataset;Prostate dataset. The results show that the proposed method outperforms the performance that can be obtained by a single set of detail coefficients. Moreover, we have shown that, also using as features the detail coefficients, a random subspace of classifiers outperforms the stand-alone classifiers. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： microarray data 1-D wavelet transform Support vector machine Fusion of classifiers

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