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Selecting informative genes from microarray data by using hybrid methods for cancer classification

ARTIFICIAL LIFE AND ROBOTICS 2009年第2期13卷 414-417页

作者： Mohamad, Mohd Saberi Omatu, Sigeru Deris, Safaai Misman, Muhammad Faiz Yoshioka, Michifumi Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai Johore Malaysia

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.

关键词： Cancer classification Genetic algorithm Gene selection Hybrid method microarray data

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Prominent feature selection of microarray data

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Progress in Natural Science:Materials International 2009年第10期19卷 1365-1371页

作者： Yihui Liu School of Computer Science and Information Technology,Shandong Institute of Light Industry,Jinan 250353,China School of Computer Science and Information Technology Shandong Institute of Light Industry Jinan 250353 China

For wavelet transform,a set of orthogonal wavelet basis aims to detect the localized changing features contained in microarray *** this research,we investigate the performance of the selected wavelet features based on wavelet detail coefficients at the second level and the third *** genetic algorithm is performed to optimize wavelet detail coefficients to select the best discriminant *** are carried out on four microarray datasets to evaluate the performance of *** results prove that wavelet features optimized from detail coefficients efficiently characterize the differences between normal tissues and cancer tissues.

关键词： microarray data Wavelet detail coefficients Feature extraction Feature selection

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Empirical Bayes analysis of unreplicated microarray data

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COMPUTATIONAL STATISTICS 2009年第3期24卷 393-408页

作者： Cho, HyungJun Kang, Jaewoo Lee, Jae K. Korea Univ Dept Stat Seoul South Korea Korea Univ Dept Biostat Seoul South Korea Korea Univ Dept Comp Sci & Engn Seoul South Korea Univ Virginia Dept Publ Hlth Sci Charlottesville VA USA

Because of the high costs of microarray experiments and the availability of only limited biological materials, microarray experiments are often performed with a small number of replicates. Investigators, therefore, often have to perform their experiments with low replication or without replication. However, the heterogeneous error variability observed in microarray experiments increases the difficulty in analyzing microarray data without replication. No current analysis techniques are practically applicable to such microarray data analysis. We here introduce a statistical method, the so-called unreplicated heterogeneous error model (UHEM) for the microarray data analysis without replication. This method is possible by utilizing many adjacent-intensity genes for estimating local error variance after nonparametric elimination of differentially expressed genes between different biological conditions. We compared the performance of UHEM with three empirical Bayes prior specification methods: between-condition local pooled error, pseudo standard error, or adaptive standard error-based HEM. We found that our unreplicated HEM method is effective for the microarray data analysis when replication of an array experiment is impractical or prohibited.

关键词： microarray data Empirical Bayes Markov chain Monte Carlo No replication

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An Iterative GASVM-Based Method: Gene Selection and Classification of microarray data

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10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Mohamad, Mohd Saberi Omatu, Sigeru Deris, Safaai Yoshioka, Michifumi Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Infomat Syst Dept Software Engn Skudai 81310 Malaysia

ISBN: (纸本)9783642024801

microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). In this study, we propose an iterative method based on hybrid genetic algorithms to select a near-optimal (smaller) subset of informative genes in classification of the microarray data. The experimental results show that our proposed method is capable in selecting the near-optimal subset to obtain better classification accuracies than other related previous works as well as four methods experimented in this work. Additionally, a list of informative genes in the best gene subsets is also presented for biological usage.

关键词： Gene selection genetic algorithm iterative method hybrid approach microarray data

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An Evolutionary Approach for Sample-Based Clustering on microarray data

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10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Glez-Pena, Daniel Diaz, Fernando Mendez, Jose R. Corchado, Juan M. Fdez-Riverola, Florentino Univ Vigo ESEI Escuela Super Ingn Informat Edificio PolitecnCampus Univ As Lagoas S-N Orense 32004 Spain Univ Valladolid Escuela Univ Informat Dept Informat E-40005 Segovia Spain Univ Salamanca Dept Informat Automat Salamanca Spain

ISBN: (纸本)9783642024801

Sample-based clustering is one of the most common methods for discovering disease subtypes as well as unknown taxonomies. By revealing hidden structures in microarray data, cluster analysis can potentially lead to more tailored therapies for patients as well as better diagnostic procedures. In this work, we present a novel method for automatically discovering clusters of samples which are coherent from a genetic point of view. Each possible cluster is characterized by a fuzzy pattern which maintains a fuzzy discretization of relevant gene expression values. Noise genes are identified and removed from the fuzzy pattern based on their probability of appearance. Possible clusters are randomly constructed and iteratively refined by following a probabilistic search and an optimization schema. Experimental results over publicly available microarray data show the effectiveness of the proposed method.

关键词： simulated annealing sample-based clustering discriminant fuzzy pattern microarray data

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Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

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BMC Medical Genomics 2009年第1期2卷 1-19页

作者： Yeh, Hsiang-Yuan Cheng, Shih-Wu Lin, Yu-Chun Yeh, Cheng-Yu Lin, Shih-Fang Soo, Von-Wun Department of Computer Science National Tsing Hua University HsinChu 300 Taiwan Institute of Information Systems and Applications National Tsing Hua University HsinChu 300 Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung 811 Taiwan

Background. Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford microarray database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. Conclusions. We provide a computat

关键词： microarray data Cluster Coefficient microarray dataset Dependent Gene Network Motif

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Accelerating Incremental Wrapper based Gene Selection with K-Nearest-Neighbor

Accelerating Incremental Wrapper based Gene Selection with K...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Aiguo Wang Ning An Guilin Chen Lian Li Gil Alterovitz The Gerontechnology Lab School of Computer and Information Hefei University of Technology School of Computer and Information Engineering Chuzhou University Center for Biomedical Informatics Harvard Medical School

ISBN: (纸本)9781479956708

Wrapper based gene selection methods tend to obtain better classification accuracy than filter methods, while it is much more time consuming. Accelerating this process without degrading the high accuracy is of great value for researchers to better analyze gene expression profiles. In this paper, we explore to reduce the time complexity of wrapper based gene selection method with K-Nearest-Neighbor (KNN) classifier embedded. Instead of taking KNN as a black box, we incrementally construct and maintain a classifier distance matrix to speed up the gene selection process. Experiments on six publicly available microarrays were first conducted to show the effectiveness of incremental wrapper based gene selection method with KNN. Then, to demonstrate the performance gain in time cost reduction, we analyzed the time complexity and experimentally evaluated it. Both theoretical analysis and experimental results prove that the proposed approach greatly accelerates the gene selection process without degrading the classification accuracy.

关键词： microarray data Gene selection K-nearest-neighbor Wrapper Filter

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An ensemble of SVM classifiers based on gene pairs

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第6期43卷 729-737页

作者： Tong, Muchenxuan Liu, Kun-Hong Xu, Chungui Ju, Wenbin Xiamen Univ Dept Elect Engn Xiamen 361005 Fujian Peoples R China Xiamen Univ Sch Software Xiamen 361005 Fujian Peoples R China Peking Univ Peoples Hosp Dept Trauma Orthopaed Beijing 100871 Peoples R China Dalian Med Univ Affiliated Hosp 2 Dept Urol Dalian Peoples R China

In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： microarray data Top scoring pair (TSP) Ensemble learning Genetic algorithm (GA) Support vector machine (SVM)

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Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第4期43卷 334-341页

作者： Prestat, Emmanuel de Morais, Sergio Rodrigues Vendrell, Julie A. Thollet, Aurelie Gautier, Christian Cohen, Pascale A. Aussem, Alex Univ Lyon F-69000 Lyon France Univ Lyon 1 F-69000 Lyon France Univ Lyon 1 CNRS UMR5558 Lab Biometrie & Biol Evolut F-69622 Villeurbanne France INRIA Rhone Alpes BAMBOO Team Rhone Alpes France Ecole Cent Lyon F-69134 Ecully France Univ Lyon 1 CNRS UMR5205 Lab Informat Image & Syst Informat F-69622 Villeurbanne France Ctr Rech Cancerol Lyon Inserm U1052 F-69000 Lyon France Ctr Rech Cancerol Lyon CNRS UMR5286 F-69000 Lyon France Ctr Leon Berard F-69000 Lyon France

In this study, we discuss and apply a novel and efficient algorithm for learning a local Bayesian network model in the vicinity of the ZNF217 oncogene from breast cancer microarray data without having to decide in advance which genes have to be included in the learning process. ZNF217 is a candidate oncogene located at 20q13, a chromosomal region frequently amplified in breast and ovarian cancer, and correlated with shorter patient survival in these cancers. To properly address the difficulties in managing complex gene interactions given our limited sample, statistical significance of edge strengths was evaluated using bootstrapping and the less reliable edges were pruned to increase the network robustness. We found that 13 out of the 35 genes associated with deregulated ZNF217 expression in breast tumours have been previously associated with survival and/or prognosis in cancers. Identifying genes involved in lipid metabolism opens new fields of investigation to decipher the molecular mechanisms driven by the ZNF217 oncogene. Moreover, nine of the 13 genes have already been identified as putative ZNF217 targets by independent biological studies. We therefore suggest that the algorithms for inferring local BNs are valuable data mining tools for unraveling complex mechanisms of biological pathways from expression data. The source code is available at http://***-lyon1. fr/similar to aaussem/***. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Machine learning Bayesian networks Feature selection microarray data Breast cancer Ovarian cancer

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Gene-Expression-Based Cancer Subtypes Prediction Through Feature Selection and Transductive SVM

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第4期60卷 1111-1117页

作者： Maulik, Ujjwal Mukhopadhyay, Anirban Chakraborty, Debasis Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Kalyani Univ Dept Comp Sci & Engn Kalyani 741235 W Bengal India Murshidabad Coll Engn & Technol Dept Elect & Commun Engn Cossimbazar 742102 Rajasthan India

With the advancement of microarray technology, gene expression profiling has shown great potential in outcome prediction for different types of cancers. microarray cancer data, organized as samples versus genes fashion, are being exploited for the classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer type. Nevertheless, small sample size remains a bottleneck to design suitable classifiers. Traditional supervised classifiers can only work with labeled data. On the other hand, a large number of microarray data that do not have adequate follow-up information are disregarded. A novel approach to combine feature (gene) selection and transductive support vector machine (TSVM) is proposed. We demonstrated that 1) potential gene markers could be identified and 2) TSVMs improved prediction accuracy as compared to the standard inductive SVMs (ISVMs). A forward greedy search algorithm based on consistency and a statistic called signal-to-noise ratio were employed to obtain the potential gene markers. The selected genes of the microarray data were then exploited to design the TSVM. Experimental results confirm the effectiveness of the proposed technique compared to the ISVM and low-density separationmethod in the area of semisupervised cancer classification as well as gene-marker identification.

关键词： Low-density separation (LDS) microarray data semisupervised classification support vector machines (SVM) transductive learning

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