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A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of dna microarray data

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第3期15卷 941-962页

作者： Houari, Amina Ben Yahia, Sadok Univ Mustapha STAMBOULI Mascara Mascara Algeria Tallinn Univ Technol Dept Software Sci Akad Tee 15a Tallinn Estonia Univ Southern Denmark Maersk Mc Kinney Moller Inst Ctr Ind Software Als 2Bldg A1-20 DK-6400 Sonderborg Denmark

Analyzing and understanding large and complex volumes of biological data is a challenging task as data becomes more widely available. In biomedical research, gene expression data are among the most commonly used biological data. Formal concept analysis frequently identifies deferentially expressed genes in microarray data. Top-K formal concepts are effective at producing effective Formal Concepts. To our knowledge, no existing algorithm can complete the difficult task of identifying only important biclusters. For this purpose, a new Top-K formal concepts-based algorithm for mining biclusters from gene expression data is proposed: Top-BicMiner. It extracts biclusters' sets with positively and negatively correlated genes according to distinct correlation measures. The proposed method is applied to both synthetic and real-life microarray datasets. The experimental results highlight the Top-BicMiner's ability to identify statistically and biologically significant biclusters.

关键词： Biclustering Formal concept analysis dna microarray data Top-K formal concepts Positive correlation Negative correlation

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Multiple SVM-RFE for Multi-class Gene Selection on dna microarray data

Multiple SVM-RFE for Multi-class Gene Selection on DNA Micro...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Li Huang, Xiaojuan Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Soochow Univ Collaborat Innovat Ctr Novel Software Technol & I Suzhou 215006 Jiangsu Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Suzhou 215006 Jiangsu Peoples R China

ISBN: (纸本)9781479919598

This paper presents a new multi-class gene selection and classification method based on multiple support vector machine recursive feature elimination (SVM-RFE). For a multi-class dna microarray problem, we solve it as multiple binary classification problems. First, the one-versus-all method is used to decompose the multi-class task into multiple binary problems. Second, an SVM-RFE is adopted to select genes for each binary problem. Then, an SVM classifier is used to train the selected gene data for a binary problem. Finally, we combine the outputs of multiple SVM classifiers. Experimental results on three dna microarray datasets show that the proposed method achieves higher classification accuracy.

关键词： support vector machine feature selection recursive feature elimination multi-class classification dna microarray data

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Multiple SVM-RFE for Multi-class Gene Selection on dna microarray data

Multiple SVM-RFE for Multi-class Gene Selection on DNA Micro...

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International Joint Conference on Neural Networks

作者： Li Zhang Xiaojuan Huang School of Computer Science and Technology & Collaborative Innovation Center of Novel Software Technology and Industrialization Soochow University Suzhou 215006 Jiangsu China School of Computer Science and Technology & Provincial Key laboratory for Computer Information Processing Soochow University Suzhou 215006 Jiangsu China

ISBN: (纸本)9781479919611

关键词： support vector machine feature selection recursive feature elimination multi-class classification dna microarray data Support Vector Network Feature extraction multi-class dna microarrays Phyla Genes Binary Classification Methods

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A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

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NEURAL COMPUTING & APPLICATIONS 2023年第16期35卷 11531-11561页

作者： Nouri-Moghaddam, Babak Ghazanfari, Mehdi Fathian, Mohammad Iran Univ Sci & Technol Dept Ind Engn Tehran *** Iran

microarray technology is known as one of the most important tools for collecting dna expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior per

关键词： Gene selection dna microarray data Hybrid method Multi-filter Multi-objective wrapper Forest optimization algorithm Ensemble classification

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An evolutionary computational model applied to cluster analysis of dna microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第7期40卷 2575-2591页

作者： Castellanos-Garzon, Jose A. Diaz, Fernando Univ Valladolid Univ Sch Comp Sci Dept Comp Sci Segovia 40005 Spain

This paper proposes a new hierarchical clustering method using genetic algorithms for the analysis of gene expression data. This method is based on the mathematical proof of several results, showing its effectiveness with regard to other clustering methods. Genetic algorithms applied to cluster analysis have disclosed good results on biological data and many studies have been carried out in this sense, although most of them are focused on partitional clustering methods. Even though there are few studies that attempt to use genetic algorithms for building hierarchical clustering, they do not include constraints that allow us to reduce the complexity of the problem. Therefore, these studies become intractable problems for large data sets. On the other hand, the deterministic hierarchical clustering methods generally face the problem of convergence towards local optimums due to their greedy strategy. The method introduced here is an alternative to solve some of the problems existing methods face. The results of the experiments have shown that our approach can be very effective in cluster analysis of dna microarray data. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： dna microarray data Genetic algorithm data mining Hierarchical clustering Cluster validity Combinatorial optimization

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Bayesian variable selection in clustering high-dimensional data via a mixture of finite mixtures

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2021年第12期91卷 2551-2568页

作者： Doo, Woojin Kim, Heeyoung Korea Adv Inst Sci & Technol KAIST Dept Ind & Syst EngnR Daejeon South Korea

When clustering high-dimensional data, it is often important to identify variables that discriminate the clusters. Meanwhile, a common issue in clustering is to determine the number of clusters. In this study, we propose a new method that simultaneously performs clustering and variable selection, while inferring the number of clusters from the data. We formulate the clustering problem using a finite mixture model with a symmetric Dirichlet weights prior, while also placing a prior on the number of components. That is, we utilize a mixture of finite mixtures. We handle the variable selection problem by introducing a latent binary vector, which represents the inclusion/exclusion of variables. We update the binary vector for variable selection using a Metropolis algorithm and perform inference on the cluster structure using a split-merge Markov chain Monte Carlo technique. We demonstrate the advantage of our method using simulated and two real dna microarray datasets.

关键词： Bayesian inference clustering dna microarray data finite mixture model high-dimensional data variable selection

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A new FCA-based method for identifying biclusters in gene expression data

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2018年第11期9卷 1879-1893页

作者： Houari, Amina Ayadi, Wassim Ben Yahia, Sadok Univ Tunis El Manar Fac Sci Tunis LIPAH LR11ES14 Tunis 2092 Tunisia Univ Tunis Natl Higher Engn Sch Tunis LaTICE LR11ES04 Tunis 1008 Tunisia

Biclustering has been very relevant within the field of gene expression data analysis. In fact, its main thrust stands in its ability to identify groups of genes that behave in the same way under a subset of samples (conditions). However, the pioneering algorithms of the literature has shown some limits in terms of the quality of unveiled biclusters. In this paper, we introduce a new algorithm, called BiFCA+, for biclustering microarray data. BiFCA+ heavily relies on the mathematical background of the formal concept analysis, in order to extract the set of biclusters. In addition, the Bond correlation measure is of use to filter out the overlapping biclusters. The extensive experiments, carried out on real-life datasets, shed light on BiFCA+'s ability to identify statistically and biologically significant biclusters.

关键词： Biclustering Formal concept analysis data mining Bioinformatics dna microarray data Bond correlation measure

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A Feature Gene Selection Method Based on ReliefF and PSO 10

A Feature Gene Selection Method Based on ReliefF and PSO

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10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Liu Mengdi Xu Liancheng Yi Jing Huang Jie Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China Shandong Prov Key Lab Distributed Comp Software N Jinan 250358 Shandong Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9781538651148

Due to the shortcomings of low efficiency and low accuracy of dna microarray data, the paper proposes a feature gene selection algorithm based on ReliefF and PSO (RefFPSO). Firstly, ReliefF is used as the feature pre-filter to delete the genes with low correlation with the classification target. Then PSO is used as the search algorithm. Finally, the classification accuracy of SVM is used as the evaluation function of the feature subset to get the final optimal gene subset. Experiments show that this method can effectively put forward irrelevant genes and use fewer characteristic genes to obtain higher classification accuracy.

关键词： dna microarray data ReliefF algorithm particle swarm optimization feature selection

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Missing value estimation for microarray data through cluster analysis

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KNOWLEDGE AND INFORMATION SYSTEMS 2017年第3期52卷 709-750页

作者： Pati, Soumen Kumar Das, Asit Kumar St Thomas Coll Engn & Technol Dept Informat Technol Kolkata India Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur Howrah India

microarray datasets with missing values need to impute accurately before analyzing diseases. The proposed method first discretizes the samples and temporarily assigns a value in missing position of a gene by the mean value of all samples in the same class. The frequencies of each gene value in both types of samples for all genes are calculated separately and if the maximum frequency occurs for same expression value in both types, then the whole gene is entered into a subset;otherwise, each portion of the gene of respective sample type (i.e., normal or disease) is entered into two separate subsets. Thus, for each gene expression value, maximum three different clusters of genes are formed. Each gene subset is further partitioned into a stable number of clusters using proposed splitting and merging clustering algorithm that overcomes the weakness of Euclidian distance metric used in high-dimensional space. Finally, similarity between a gene with missing values and centroids of the clusters are measured and the missing values are estimated by corresponding expression values of a centroid having maximum similarity. The method is compared with various statistical, cluster-based and regression-based methods with respect to statistical and biological metrics using microarray datasets to measure its effectiveness.

关键词： dna microarray data High-dimensional space Clustering algorithm Similarity measurement Missing value imputation

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Classifying dna microarray for Cancer Diagnosis via Method Based on Complex Networks 1

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13th International Conference on Intelligent Computing (ICIC)

作者： Wu, Peng Dong, Likai Fan, Yuling Wang, Dong Univ Jinan Sch Informat Sci & Engn Jinan Shandong Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Shandong Peoples R China

ISBN: (数字)9783319633121

ISBN: (纸本)9783319633121;9783319633114

Performing microarray expression data classification can improve the accuracy of a cancer diagnosis. The varying technique including Support Vector Machines (SVMs), Neuro-Fuzzy models (NF), K-Nearest Neighbor (KNN), Neural Network (NN), and etc. have been applied to analyze microarray expression data. In this investigation, a novel complex network classifier is proposed to do such thing. To build the complex network classifier, we tried a hybrid method based on the Particle Swarm Optimization algorithm (PSO) and Genetic Programming (GP), of which GP aims at finding an optimal structure and PSO accomplishes the fine tuning of the parameters encoded in the proposed classifier. The experimental results conducted on Leukemia and Colon data sets are comparable to the state-of-the-art outcomes.

关键词： dna microarray data Complex network classifier Particle Swarm Optimization algorithm Genetic Programming

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