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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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A Novel Hybrid Filter/Wrapper Feature Selection Approach Based on Improved Fruit Fly Optimization Algorithm and Chi-square Test for High Dimensional microarray data

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CURRENT BIOINFORMATICS 2021年第1期16卷 63-79页

作者： Yan, Chaokun Wu, Bin Ma, Jingjing Zhang, Ge Luo, Junwei Wang, Jianlin Luo, Huimin Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China China Mobile Online Serv Co Ltd Hong Kong Peoples R China Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Henan Peoples R China

microarray data is widely utilized for disease analysis and diagnosis. However, it is hard to process them directly and achieve high classification accuracy due to the intrinsic characteristics of high dimensionality and small size samples. As an important data preprocessing technique, feature selection is usually used to reduce the dimensionality of some datasets. Given the limitations of employing filter or wrapper approaches individually for feature selection, in the study, a novel hybrid filter-wrapper approach is proposed for high dimensional datasets. First, the Chi-square Test is utilized to filter out some irrelevant or redundant features. Next, an improved binary Fruit Fly Optimization algorithm is conducted to further search the optimal feature subset without degrading the classification accuracy. Here, the KNN classifier with the 10-fold-CV is utilized to evaluate the classification accuracy. Extensive experimental results on six benchmark biomedical datasets show that our proposed approach can achieve superior performance compared with other state-of-the-art methods.

关键词： Feature selection fruit fly optimization algorithm Chi-square test levy flight gaussian mutation microarray data

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Assessing gene stability and gene affinity in microarray data classification using an extended relieff algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第15期83卷 45761页

作者： Srivastava, Neha Tayal, Devendra K. Indira Gandhi Delhi Tech Univ Women James ChurchNew Church RdOpp StKashmere Gate New Delhi 110006 Delhi India

microarray data have become an integral part of the clinical and drug discovery process. Due to its voluminous and heterogeneous nature, the question arises of the interpretability and stability of the traditional gene selection method. To enhance the stability of the gene selection method, so that the results are better explicable, an ameliorated Extended ReliefF gene selection algorithm is proposed. It encodes gene affinity information using a new mathematical formula based on Bayes' theorem and Manhattan distance for calculating the nearest neighbor in a pooled sample. It works in four aspects: initializing sample gene weight, improving gene weight, maximizing sample gene weight and finally adopting mutation operation. The proposed method selects the most informative genes which are highly perceptive to the prognosis of the disease. Further, to accomplish the accuracy and stability of the algorithm, soft classification is performed on Relieved_F, STIR, VLS-RelifF, I-RelieF, conventional ReliefF and proposed extended ReliefF algorithms using three classifiers namely Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) on ten microarray datasets. According to the findings, MLP training times are much longer than those of RF and SVM. From a network perspective, SVM is much faster at training, whereas MLP excels in terms of accuracy. With a rise in gene similarity among the genes selected from the multiple training sets, the approach becomes more stable. As a result, it can be seen that the recommended gene selection algorithm greatly outperforms the other feature selection methods in terms of accuracy and stability.

关键词： Gene selection Gene affinity information microarray data ReliefF method SVM MLP

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Novel techniques for microarray data analysis: Probabilistic Principal Surfaces and Competitive Evolution on data

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2005年第4期2卷 514-523页

作者： Amato, R Ciaramella, A Del Mondo, C De Vinco, L Donalek, C Longo, G Miele, G Raiconi, G Staiano, A Tagliaferri, R Univ Salerno Dept Math & Informat Fisciano Sa Italy Univ Naples Federico II Dept Phys Sci Naples Italy Univ Naples Federico II Dept Math & Applicat Naples Italy Ist Nazl Fis Nucl Italian Inst Nucl Phys Unit Naples I-80125 Naples Italy INAF Italian Inst Astrophys Naples Italy

microarrays are among the most powerful tools in biological research, but in order to attain its full potentialities, it is imperative to develop techniques capable to effectively exploit the huge quantity of data which they produce. In this paper two machine learning methodologies for microarray data analysis are proposed: (1) Probabilistic Principal Surfaces (PPS), which is a nonlinear latent variable model which offers very appealing visualization and classification abilities and can be effectively employed for clustering purposes. More specifically, the PPS method builds a probability density function of a given data set of patterns, lying in a D-dimensional space (with D >> 3), expressed in terms of a fixed number of latent variables, lying in a Q-dimensional space (Q is usually 2 or 3), which can be used (after a proper manipulation) to visualize, classify and cluster the data;(2) Competitive Evolution on data (CED) is instead an evolutionary system in which the possible solutions (cluster centroids) compete to conquer the largest possible number of resources (data) and thus partition the input data set in clusters. We discuss the application of both methods to the analysis of microarray data obtained for the yeast genome.

关键词： microarray data data mining latent variable models evolutionary algorithms visualization clustering

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Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data

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BMC BIOINFORMATICS 2008年第1期9卷 1-13页

作者： Xiong, Huiling Zhang, Dapeng Martyniuk, Christopher J. Trudeau, Vance L. Xia, Xuhua Univ Ottawa Dept Biol CAREG Ottawa ON K1N 6N5 Canada

Background: Normalization is essential in dual-labelled microarray data analysis to remove non-biological variations and systematic biases. Many normalization methods have been used to remove such biases within slides ( Global, Lowess) and across slides ( Scale, Quantile and VSN). However, all these popular approaches have critical assumptions about data distribution, which is often not valid in practice. Results: In this study, we propose a novel assumption-free normalization method based on the Generalized Procrustes Analysis ( GPA) algorithm. Using experimental and simulated normal microarray data and boutique array data, we systemically evaluate the ability of the GPA method in normalization compared with six other popular normalization methods including Global, Lowess, Scale, Quantile, VSN, and one boutique array-specific housekeeping gene method. The assessment of these methods is based on three different empirical criteria: across-slide variability, the Kolmogorov-Smirnov ( K-S) statistic and the mean square error ( MSE). Compared with other methods, the GPA method performs effectively and consistently better in reducing across-slide variability and removing systematic bias. Conclusion: The GPA method is an effective normalization approach for microarray data analysis. In particular, it is free from the statistical and biological assumptions inherent in other normalization methods that are often difficult to validate. Therefore, the GPA method has a major advantage in that it can be applied to diverse types of array sets, especially to the boutique array where the majority of genes may be differentially expressed.

关键词： Mean Square Error microarray data Normalization Method Reference Array Generalize Procrustes Analysis

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MicroCluster: Efficient deterministic biclustering of microarray data

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IEEE INTELLIGENT SYSTEMS 2005年第6期20卷 40-+页

作者： Zhao, LZ Zaki, MJ Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA

MicroCluster can mine different types of arbitrarily positioned and overlapping clusters of genetic data to find interesting patterns. Our approach has four key features. First, we mine only the maximal biclusters satisfying certain homogeneity criteria. Second, the clusters can be arbitrarily positioned anywhere in the input data matrix, and they can have arbitrary overlapping regions. Third, MicroCluster uses a flexible definition of a cluster that lets it mine several types of biclusters (which previously were studied independently). Finally, MicroCluster can delete or merge biclusters that have large overlaps. So, it can tolerate some noise in the data set and let users focus on the most important clusters. We\'ve developed a set of metrics to evaluate the clustering quality and have tested MicroCluster\'s effectiveness on several synthetic and real data sets.

关键词： biology computing data mining genetics pattern clustering MicroCluster deterministic biclustering genetic data mining microarray data bicluster bioinformatics clustering gene expression microarrays microarray Genetics Bioinformatics data mining dataset Cluster Analysis biology computing

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Local likelihood regression in generalized linear single-index models with applications to microarray data

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COMPUTATIONAL STATISTICS & data ANALYSIS 2006年第3期51卷 2091-2113页

作者： Lambert-Lacroix, Sophie Peyre, Julie IMAG LMC F-38041 Grenoble 9 France

Searching for an effective dimension reduction space is an important problem in regression, especially for high-dimensional data such as microarray data. A major characteristic of microarray data consists in the small number of observations n and a very large number of genes p. This "large p, small n" paradigm makes the discriminant analysis for classification difficult. In order to offset this dimensionality problem a solution consists in reducing the dimension. Supervised classification is understood as a regression problem with a small number of observations and a large number of covariates. A new approach for dimension reduction is proposed. This is based on a semi-parametric approach which uses local likelihood estimates for single-index generalized linear models. The asymptotic properties of this procedure are considered and its asymptotic performances are illustrated by simulations. Applications of this method when applied to binary and multiclass classification of the three real data sets Colon, Leukemia and SRBCT are presented. (c) 2006 Elsevier B.V. All rights reserved.

关键词： dimension reduction generalized linear models generalized linear single-index models local likelihood estimates nonparametric regression microarray data

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Genetic algorithm based cancerous gene identification from microarray data using ensemble of filter methods

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2019年第1期57卷 159-176页

作者： Ghosh, Manosij Adhikary, Sukdev Ghosh, Kushal Kanti Sardar, Aritra Begum, Shemim Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata India Govt Coll Engn & Text Technol Dept Comp Sci & Engn Berhampur W Bengal India

microarray datasets play a crucial role in cancer detection. But the high dimension of these datasets makes the classification challenging due to the presence of many irrelevant and redundant features. Hence, feature selection becomes irreplaceable in this field because of its ability to remove the unrequired features from the system. As the task of selecting the optimal number of features is an NP-hard problem, hence, some meta-heuristic search technique helps to cope up with this problem. In this paper, we propose a 2-stage model for feature selection in microarray datasets. The ranking of the genes for the different filter methods are quite diverse and effectiveness of rankings is datasets dependent. First, we develop an ensemble of filter methods by considering the union and intersection of the top-n features of ReliefF, chi-square, and symmetrical uncertainty. This ensemble allows us to combine all the information of the three rankings together in a subset. In the next stage, we use genetic algorithm (GA) on the union and intersection to get the fine-tuned results, and union performs better than the latter. Our model has been shown to be classifier independent through the use of three classifiersmulti-layer perceptron (MLP), support vector machine (SVM), and K-nearest neighbor (K-NN). We have tested our model on five cancer datasetscolon, lung, leukemia, SRBCT, and prostate. Experimental results illustrate the superiority of our model in comparison to state-of-the-art methods.

关键词： Wrapper method Filter method Ensemble microarray data Cancer detection

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Partial maximum correlation information: A new feature selection method for microarray data classification

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NEUROCOMPUTING 2019年 323卷 231-243页

作者： Yuan, Mingshun Yang, Zijiang Ji, Guoli Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China York Univ Sch Informat Technol Toronto ON M3J 1P3 Canada Xiamen Univ Innovat Ctr Cell Signaling Network Xiamen 361102 Fujian Peoples R China

Feature (gene) selection of microarray data is a very important and challenging task. This paper proposes a new feature selection method, partial maximum correlation information (PMCI), for microarray data classification. The proposed method extracts several orthogonal components from the feature space to evaluate the importance of each feature. In PMCI method, the component extraction is based on the correlation between feature space and class coding space. Meanwhile, this paper also provides a new generic class-encoding scheme for multi-class problems. This type of encoding scheme can ensure the smooth implementation for PMCI and has a clear geometric meaning. To validate the performance of the proposed method, this paper selects six state-of-the-art algorithms for comparison based on ten widely used microarray benchmark datasets for experiment. The experimental results show that PMCI is an effective feature selection method with good performance and lower time complexity. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Feature selection microarray data SIMPIS Partial maximum correlation information (PMCI)

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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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