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Can classification performance be predicted by complexity measures? A study using microarray data

KNOWLEDGE AND INFORMATION SYSTEMS 2017年第3期51卷 1067-1090页

作者： Moran-Fernandez, L. Bolon-Canedo, V. Alonso-Betanzos, A. Univ A Coruna Dept Comp Sci Lab Res & Dev Artificial Intelligence LIDIA La Coruna 15071 Spain

data complexity analysis enables an understanding of whether classification performance could be affected, not by algorithm limitations, but by intrinsic data characteristics. microarray datasets based on high numbers of gene expressions combined with small sample sizes represent a particular challenge for machine learning researchers. This type of data also has other particularities that may negatively affect the generalization capacity of classifiers, such as overlaps between classes and class imbalance. Making use of several complexity measures, we analyzed the intrinsic complexity of several microarray datasets with and without feature selection and then explored the connection with the empirical results obtained by four widely used classifiers. Experimental results for 21 binary and multiclass datasets demonstrate that a correlation exists between microarray data complexity and the classification error rates.

关键词： data complexity measures Classification microarray data Feature selection Filters

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A Ll-regularized feature selection method for local dimension reduction on microarray data

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2017年 67卷 92-101页

作者： Guo, Shun Guo, Donghui Chen, Lifei Jiang, Qingshan Xiamen Univ Dept Elect Engn Fujian 361005 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China Fujian Normal Univ Sch Math & Comp Sci Fujian 350117 Peoples R China

Dimension reduction is a crucial technique in machine learning and data mining, which is widely used in areas of medicine, bioinformatics and genetics. In this paper, we propose a two-stage local dimension reduction approach for classification on microarray data. In first stage, a new Li-regularized feature selection method is defined to remove irrelevant and redundant features and to select the important features (biomarkers). In the next stage, PLS-based feature extraction is implemented on the selected features to extract synthesis features that best reflect discriminating characteristics for classification. The suitability of the proposal is demonstrated in an empirical study done with ten widely used microarray datasets, and the results show its effectiveness and competitiveness compared with four state-of-the-art methods. The experimental results on St Jude dataset shows that our method can be effectively applied to microarray data analysis for subtype prediction and the discovery of gene coexpression. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Local dimension reduction Classification Ll-regularized logistic regression microarray data Partial least squares (PLS)

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Boosting Support Vector Machines for Imbalanced microarray data

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Procedia Computer Science 2018年 144卷 174-183页

作者： Risky Frasetio Wahyu Pratama Santi Wulan Purnami Santi Puteri Rahayu Department of Statistics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya 60111 Indonesia

Nowadays, microarray data plays an important role in the detection and classification of almost all types of cancer tissue. The gene expression produced by microarray technology that carries the information from genes is then matched to a specific cancer condition. The problems that often appear in the classification using microarray data are high-dimensional data and imbalanced class. The problem of high-dimensional data can be solved by using Fast Correlated Based Filter (FCBF) feature selection. In this paper, Support Vector Machine (SVM) classifier is used because of its advantages. However, some studies mention that almost all classifier model including SVM are sensitive with respect to imbalanced class. Synthetic Minority Oversampling Technique (SMOTE) is one of the prepocessing data methods in handling imbalanced class based on sampling approach by increasing the number of samples from the minority class. This method often works well but sometimes it might suffer from over-fitting problem. One other alternative approach in improving the performance of imbalanced data classification is boosting. This method constructs a powerful final classifier by combining a set of SVMs as base classifier during the iteration process. So, it can improve the classification performance. In this study, colon cancer and myeloma data are used in the analysis. The results show that SMOTEBoost with SVM as base classifier outperforms SVM, SMOTE-SVM, and AdaBoost with SVM as base classifier by looking on G-mean metric.

关键词： microarray data Imbalanced Class SVM SMOTE AdaBoost SMOTEBoost

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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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A Gene Selection Method for microarray data Based on Binary PSO Encoding Gene-to-Class Sensitivity Information

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第1期14卷 85-96页

作者： Han, Fei Yang, Chun Wu, Ya-Qi Zhu, Jian-Sheng Ling, Qing-Hua Song, Yu-Qing Huang, De-Shuang Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci & Engn Zhenjiang 212013 Jiangsu Peoples R China Tongji Univ Inst Machine Learning & Syst Biol Sch Elect & Informat Engn Shanghai 200092 Peoples R China

Traditional gene selection methods for microarray data mainly considered the features' relevance by evaluating their utility for achieving accurate predication or exploiting data variance and distribution, and the selected genes were usually poorly explicable. To improve the interpretability of the selected genes as well as prediction accuracy, an improved gene selection method based on binary particle swarm optimization (BPSO) and prior information is proposed in this paper. In the proposed method, BPSO encoding gene-to-class sensitivity (GCS) information is used to perform gene selection. The gene-to-class sensitivity information, extracted from the samples by extreme learning machine (ELM), is encoded into the selection process in four aspects: initializing particles, updating the particles, modifying maximum velocity, and adopting mutation operation adaptively. Constrained by the gene-to-class sensitivity information, the new method can select functional gene subsets which are significantly sensitive to the samples' classes. With the few discriminative genes selected by the proposed method, ELM, K-nearest neighbor and support vector machine classifiers achieve much high prediction accuracy on five public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

关键词： Binary particle swarm optimization gene selection gene-to-class sensitivity information microarray data

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Gene expression microarray data from mouse CBS treated with rTMS for 30 days, mouse cerebrum and CBS treated with rTMS for 40 days

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data IN BRIEF 2018年 17卷 1078-1081页

作者： Ikeda, Tetsurou Kobayashi, Satoru Morimoto, Chikao RIKEN Brain Sci Inst Lab Struct Neuropathol 2-1 Hirosawa Wako Saitama 3510198 Japan Univ Tokyo Inst Med Sci Clin Immunol Minato Ku 4-6-1 Shirokanedai Tokyo 1088639 Japan Thermo Fisher Sci KK Tech Support Kanagawa Ku 3-9 Moriya Yokohama Kanagawa 2210022 Japan

This data article contains complementary tables related to the research article study entitled, 'Effects of repetitive transcranial magnetic stimulation on ER stress-related genes and glutamate, gamma-aminobutyric acid, and glycine transporter genes in mouse brain' (Ikeda et al. (2017) [1]), which showed that rTMS modulates glutamate, GABA and glycine transporters and regulates ER stress-related genes. Here, we provide accompanying data collected using Affymetrix GeneChip microarrays to identify changes in gene expression in mouse CBS treated with rTMS for 30 days (Tables 1- 21) and mouse cerebrum (Tables 22-57) and CBS (Tables 58-94) treated with rTMS for 40 days. (C) 2018 The Authors. Published by Elsevier Inc.

关键词： rTMS microarray data

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Gene selection and classification of microarray data: A Pareto DE approach

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2017年第1期11卷 93-107页

作者： Dash, Rasmita Misra, Bijan Siksha O Anusandhan Univ Dept Comp Sci & Informat Technol Bhubaneswar 751030 Odisha India Silicon Inst Technol Dept Comp Sci & Engn Bhubaneswar 751024 Odisha India

Sample classification is a most critical task in microarray data analysis. But representation of microarray data with the huge search space of thousands of gene makes this work more complex and difficult. To handle this problem both an efficient gene selection technique and efficient classifier is required. In this paper, we propose a multi-criterion Pareto differential evolution technique for feature selection. This technique first uses a wrapper technique i.e. a population based differential evolution gene selection (DEGS) algorithm for feature selection. The motivation of choosing differential evolution as compared to other learning technique is it tries to assign optimal ranks to each gene using probability distribution factor present in the microarray dataset using classification error as the fitness function. It is observed that these selections contain relevant genes as well as some irrelevant genes. So in the second phase bi-objective filter technique, called as Pareto based optimization is used to select minimum number of top ranked genes in the feature selection. Here we have considered information gain (IG) and Signal to noise ratio (SNR) as two objective functions for Pareto optimization. To verify the importance and relevance of the selected genes, classification using K-nearest neighbour (KNN), naive Bayesian classifier (NB), artificial neural network (ANN) and support vector machine (SVM) is done. Our experiment is conducted over four well known microarray dataset. The experimental work shows that the proposed method is better than the existing searching method in terms of both classification error and predicted feature sets. The classification result shows that performance of SVM classifier is better than the result obtained using KNN, NB and ANN. Finally this method highlights its performance in terms of both relevance of genes and classification output.

关键词： Feature selection microarray data filter method wrapper method differential evolution pareto optimal solution

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Evaluation of artificial time series microarray data for dynamic gene regulatory network inference

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JOURNAL OF THEORETICAL BIOLOGY 2017年 426卷 1-16页

作者： Xenitidis, P. Seimenis, I. Kakolyris, S. Adamopoulos, A. Democritus Univ Thrace Sch Med Med Phys Lab Alexandroupolis Greece Democritus Univ Thrace Sch Med Dept Med Oncol Alexandroupolis Greece

High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time ddmain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. (C) 2017 Elsevier Ltd. All

关键词： Gene regulatory networks Inference microarray data

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An ECOC Approach for microarray data Classification Based on Minimizing Feature Related Complexities 10

An ECOC Approach for Microarray Data Classification Based on...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Sun, Mengxin Liu, Kunhong Hong, Qingqi Xiamen Univ Software Sch Xiamen Peoples R China

ISBN: (纸本)9781538636756

The classification of multi-class microarray datasets is a hard task due to the small sample size and data unbalance problem in each class. A new Error Correcting Output Coding (ECOC) is proposed to tackle this problem by exploring data distributions based on data complexity theory. In our framework, multiple classes are randomly divided into two separate groups at first, and then overlapping region between groups are constantly reduced by exchanging classes selected by corresponding adjustment algorithms. The algorithm would produce an optimal ECOC coding matrix with high discriminative capability by minimizing feature related complexity measure. Experiments are carried out on six microarray datasets, and results demonstrate the effectiveness of our method in comparisons with four ECOC methods algorithms.

关键词： ECOC data Complexity microarray data multi class

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A Combined Clustering and Ranking based Gene Selection Algorithm for microarray data Classification 8

A Combined Clustering and Ranking based Gene Selection Algor...

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8th IEEE International Conference on Computational Intelligence and Computing Research (IEEE ICCIC)

作者： Rani, M. Jansi Devaraj, D. Kalasalingam Univ Krishnankoil Virudhunagar India Kalasalingam Univ Sch Elect & Elect Technol Krishnankoil Virudhunagar India

ISBN: (纸本)9781509066216

Biological information related to cancer patients are recorded as microarray data. data mining plays important role in gene selection and classification of microarray data. The mining information obtained from cancer dataset should be precise or accurate as it is one of the critical diseases affecting living beings. This paper proposes a combined gene selection approach for selecting most promising genes from microarray cancer data that identifies genes based on Significance, T-statistics and Signal-to-noise ratio. Two variations of gene selection is used here;Clustered gene selection that uses clustering mechanism to cluster similar genes before applying gene selection, and Non-clustered gene selection that selects genes without clustering. Selected genes are sent to Support Vector Machine for classification. Experiments have been conducted on microarray cancer data that contains binary class. Comparison with existing methods shows that proposed gene selection algorithm increases overall classification accuracy up to 5%.

关键词： microarray data support vector machine gene classification gene selection bioinformatics

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