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Ensemble component selection for improving ICA based microarray data prediction models

PATTERN RECOGNITION 2009年第7期42卷 1274-1283页

作者： Liu, Kun-Hong Li, Bo Zhang, Jun Du, Ji-Xiang Xiamen Univ Sch Software Xiamen 361005 Fujian Peoples R China Wuhan Univ Sci & Techol Sch Comp Sci Technol Wuhan 430081 Hubei Peoples R China Anhui Univ Sch Elect Sci & Technol Hefei Anhui Peoples R China Huaqiao Univ Dept Comp Sci & Technol Quanzhou 362021 Fujian Peoples R China

Independent component analysis (ICA) has been widely used to tackle the microarray dataset classification problem, but there still exists an unsolved problem that the independent component (IC) sets may not be reproducible after different ICA transformations. Inspired by the idea of ensemble feature selection, we design an ICA based ensemble learning system to fully utilize the difference among different IC sets. In this system, some IC sets are generated by different ICA transformations firstly. A multi-objective genetic algorithm (MOGA) is designed to select different biologically significant IC subsets from these IC sets, which are then applied to build base classifiers. Three schemes are used to fuse these base classifiers. The first fusion scheme is to combine all individuals in the final generation of the MOCA. In addition, in the evolution, we design a global-recording technique to record the best IC subsets of each IC set in a global-recording list. Then the IC subsets in the list are deployed to build base classifier so as to implement the second fusion scheme. Furthermore, by pruning about half of less accurate base classifiers obtained by the second scheme, a compact and more accurate ensemble system is built, which is regarded as the third fusion scheme. Three microarray datasets are used to test the ensemble systems, and the corresponding results demonstrate that these ensemble schemes can further improve the performance of the ICA based classification model, and the third fusion scheme leads to the most accurate ensemble system with the smallest ensemble size. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： microarray data Independent component analysis (ICA) Multiple classifier system (MCS) Ensemble component selection Multi-objective genetic algorithm (MOGA) Global-recording technique

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Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 4661-4667页

作者： Lee, Chien-Pang Lin, Wen-Shin Chen, Yuh-Min Kuo, Bo-Jein Natl Chung Hsing Univ Dept Agron Div Biometry Taichung 40227 Taiwan China Med Univ Sch Nursing Taichung 40402 Taiwan

Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey some of the assumptions of parametric statistical methods, or type I error may be over expanded. Therefore, our aim is to establish a gene selection method without assumption restriction to reduce the dimension of the data set. In our study, adaptive genetic algorithm/k-nearest neighbor (AGA/KNN) was used to evolve gene subsets. We find that AGA/KNN can reduce the dimension of the data set, and all test samples can be classified correctly. In addition, the accuracy of AGA/KNN is higher than that of GA/KNN, and it only takes half the CPU time of GA/KNN. After using the proposed method, biologists can identify the relevant genes efficiently from the sub-gene set and classify the test samples correctly. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Gene selection Sample classification Adaptive genetic algorithm k-Nearest neighbor microarray data

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Missing value estimation of microarray data using Sim-GAN

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KNOWLEDGE AND INFORMATION SYSTEMS 2022年第10期64卷 2661-2687页

作者： Pati, Soumen Kumar Gupta, Manan Kumar Shai, Rinita Banerjee, Ayan Ghosh, Arijit Maulana Abul Kalam Azad Univ Technol Dept Bioinformat Haringhata 741249 WB India Calcutta Univ Behala Coll Dept Math Kolkata 700060 WB India Jalpaiguri Govt Engn Coll Dept Comp Sci & Engn Jalpaiguri 735102 WB India Calcutta Inst Engn & Management Dept Elect & Commun Engn Kolkata 700040 WB India

microarray data analysis needs utmost care as it plays a significant role in cancer study. Due to the excessive complexity of the data extraction process, it loses some relevant information (missing values) which leads to a significant irrecoverable disruption from the actual scenario. The imputation of missing values is a crucial preprocessing step in analyzing microarray data. Currently, numerous methodologies have been designed to resolve the problem, but the unsatisfactory outcome is obtained with high missing rates of data. In order to estimate the missing expression to complete the dataset, a novel method has been proposed based on the similarity index and generative adversarial network (Sim-GAN). Firstly, the raw dataset has been divided into two subsets, i.e., the target set (which contains genes with missing expression values) and the candidate set (contains without missing values). In the next step, the similarity index between target genes and candidate genes has been obtained. As microarray data represents several biological factors, three similarity matrices (structural similarity, functional similarity, and semantic similarity) have been derived to find the small subset of candidate genes for each target gene. In structural similarity, a novel approach has been used to reduce the time complexity is O(1) as well as tackle the nonlinearity. Now, the obtained subsets are fed into a generative adversarial network to compute the missing values of the targeted genomes. The experimental outcomes consolidate the claim that the proposed methodology gives a satisfactory performance in terms of meaningful expression values. A detailed comparative study based on several statistical (i.e., NRMSE, AUROC, etc.) and biological (i.e., CPP, BLCI) metrics to confirm that the proposed Sim-GAN outperforms the existing missing value estimation techniques.

关键词： microarray data Missing value Structural similarity Functional similarity Semantic similarity Generative adversarial network

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Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2731-2750页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test Q

关键词： Gene expression Feature selection Moth flame optimization algorithm Quantum computing microarray data Cancer classification Bio-inspired algorithms Molecular biology Optimization algorithms Evolutionary algorithms Swarm intelligence

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Sequential local least squares imputation estimating missing value of microarray data

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COMPUTERS IN BIOLOGY AND MEDICINE 2008年第10期38卷 1112-1120页

作者： Zhang, Xiaobai Song, Xiaofeng Wang, Huinan Zhan, Huanping Nanjing Univ Aeronaut & Astronaut Dept Biomed Engn Nanjing 210016 Peoples R China

Missing values in microarray data can significantly affect subsequent analysis, thus it is important to estimate these missing values accurately. In this paper, a sequential local least squares imputation (SLLSimpute) method is proposed to solve this problem. It estimates missing values sequentially from the gene containing the fewest missing values and partially utilizes these estimated values. In addition, an automatic parameter selection algorithm, which can generate an appropriate number of neighboring genes for each target gene, is presented for parameter estimation. Experimental results confirmed that SLLSimpute method exhibited better estimation ability compared with other currently used imputation methods. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Missing value estimation Imputation method Least squares principle Normalized root mean squared error (NRMSE) microarray data

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Feature Selection with Fluid Mechanics Inspired Particle Swarm Optimization for microarray data

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Journal of Beijing Institute of Technology 2017年第4期26卷 517-524页

作者： Shengsheng Wang Ruyi Dong College of Computer Science and Technology Jilin UniversityChangchun 130012China Jilin Vocational College of Industry and Technology JilinJilin 132013China

Deoxyribonucleic acid（ DNA） microarray gene expression data has been widely utilized in the field of functional genomics,since it is helpful to study cancer,cells,tissues,organisms *** the sample sizes are relatively small compared to the number of genes,so feature selection is very necessary to reduce complexity and increase the classification accuracy of samples. In this paper,a completely newimprovement over particle swarm optimization（ PSO） based on fluid mechanics is proposed for the feature selection. This newimprovement simulates the spontaneous process of the air from high pressure to lowpressure,therefore it allows for a search through all possible solution spaces and prevents particles from getting trapped in a local optimum. The experiment shows that,this newimproved algorithm had an elaborate feature simplification which achieved a very precise and significant accuracy in the classification of 8 among the 11 datasets,and it is much better in comparison with other methods for feature selection.

关键词： feature selection particle swarm optimization （PSO） fluid mechanics （FM） microarray data support vector machine （SVM）

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GEPAS, a web-based tool for microarray data analysis and interpretation

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NUCLEIC ACIDS RESEARCH 2008年第Web Server issue期36卷 W308-W314页

作者： Tarraga, Joaquin Medina, Ignacio Carbonell, Jose Huerta-Cepas, Jaime Minguez, Pablo Alloza, Eva Al-Shahrour, Fatima Vegas-Azcarate, Susana Goetz, Stefan Escobar, Pablo Garcia-Garcia, Francisco Conesa, Ana Montaner, David Dopazo, Joaquin CIPF Bioinformat Dept E-46013 Valencia Spain CIPF INB Funct Genom Node E-46013 Valencia Spain CIPF Ctr Biomed Res Rare Dis CIBERER E-46013 Valencia Spain Univ Carlos III Madrid Dept Stat E-28903 Getafe Spain

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://***.

关键词： .clustering module microarray data normalization class prediction web services GEPAS pipeliner

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Identification of differentially expressed genes in microarray data in a principal component space

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SPRINGERPLUS 2013年第1期2卷 1页

作者： Ospina, Luis Lopez-Kleine, Liliana Univ Nacl Colombia Dept Estadist Bogota Colombia

microarray experiments are often conducted in order to compare gene expression between two conditions. Tests to detected mean differential expression of genes between conditions are conducted applying correction for multiple testing. Seldom, relationships between gene expression and microarray conditions are investigated in a multivariate approach. Here we propose determining the relationship between genes and conditions using a Principal Component Analysis (PCA) space and classifying genes to one of two biological conditions based on their position relative to a direction on the PC space representing each condition.

关键词： C-d closeness measure Differentially expressed genes microarray data Principal component analysis

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A comparison of regularization methods applied to the linear discriminant function with high-dimensional microarray data

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2013年第3期83卷 581-596页

作者： Ramey, John A. Young, Phil D. Pacific NW Natl Lab Richland WA 99352 USA Baylor Univ Dept Stat Sci Waco TX 76798 USA

Classification of gene expression microarray data is important in the diagnosis of diseases such as cancer, but often the analysis of microarray data presents difficult challenges because the gene expression dimension is typically much larger than the sample size. Consequently, classification methods for microarray data often rely on regularization techniques to stabilize the classifier for improved classification performance. In particular, numerous regularization techniques, such as covariance-matrix regularization, are available, which, in practice, lead to a difficult choice of regularization methods. In this paper, we compare the classification performance of five covariance-matrix regularization methods applied to the linear discriminant function using two simulated high-dimensional data sets and five well-known, high-dimensional microarray data sets. In our simulation study, we found the minimum distance empirical Bayes method reported in Srivastava and Kubokawa [Comparison of discrimination methods for high dimensional data, J. Japan Statist. Soc. 37(1) (2007), pp. 123134], and the new linear discriminant analysis reported in Thomaz, Kitani, and Gillies [A Maximum Uncertainty LDA-based approach for Limited Sample Size problems with application to Face Recognition, J. Braz. Comput. Soc. 12(1) (2006), pp. 112], to perform consistently well and often outperform three other prominent regularization methods. Finally, we conclude with some recommendations for practitioners.

关键词： linear discriminant analysis covariance-matrix regularization high-dimensional supervised classification microarray data expected error rate

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Improving Clustering of MicroRNA microarray data by Incorporating Functional Similarity

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CURRENT BIOINFORMATICS 2018年第1期13卷 34-41页

作者： Yang, Yang Wu, Zhichen Kong, Wei Shanghai Jiao Tong Univ Dept Comp Sci & Engn 800 Dong Chuan Rd Shanghai 200240 Peoples R China Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Shanghai Maritime Univ Dept Comp Sci & Engn 1550 Haigang Ave Shanghai Peoples R China Shanghai Jiao Tong Univ Minist Educ Shanghai Ctr Syst Biomed Key Lab Syst Biomed Shanghai 200240 Peoples R China

Background: MicroRNAs (miRNAs) are short non-coding RNAs that serve as key regulators at post-transcriptional level in many important biological processes. In recent years, miRNA expression profiles have been largely investigated and demonstrated to be promising biomarkers for discriminating subtypes of complex human diseases and measuring treatment effects. Methods: Most of the analysis approaches for DNA microarray data can be applied to miRNA microarray data, such as statistical test for differential expression analysis, and clustering for co-regulation analysis. Benefitting from the comprehensive annotation available for protein-coding genes, gene expression analysis is usually guided by prior biological knowledge in order to obtain more biologically meaningful results. However, functional annotation of miRNAs is relatively few, thus the prior knowledge-based methods are hard to be applied to miRNAs. In this paper, we incorporate gene ontology information of the target genes of miRNAs for the clustering of miRNAs, and propose a combined similarity measure. Results: The experiments were conducted on two public miRNA microarray data sets. Experimental results show that the new similarity measure can improve the quality of clustering with regard to the classification accuracy and functional enrichment significance of clusters. Conclusion: The clustering of microRNA expression profiles can be improved by incorporating domain knowledge, thus resulting in more functionally compact clusters, which are the basis for the identification of potential miRNA biomarkers and the construction of miRNA co-regulation networks.

关键词： microRNA clustering functional similarity biomarkers microarray data functional annotation

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