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Estimation of Missing Values Using Hybrid Fuzzy Clustering Mean and Majority Vote for microarray data

Procedia Computer Science 2019年 163卷 145-153页

作者： Shamini Raja Kumaran Mohd Shahizan Othman Lizawati Mi Yusuf Arda Yunianta School of Computing Faculty Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Faculty of Computing and Information Technology Rabigh King Abdulaziz University Jeddah Makkah Saudi Arabia

Rapid development in microarray experiments leads toward generating a large amount of genes expressions. Handling such a massive amount of gene expressions is a challenge using traditional methods especially multiple missing expression values exist. This article proposed a novel hybrid fuzzy clustering mean and majority vote to yield better performance. The proposed method will be evaluated with other three imputation methods and trained with four publicly available microarray datasets. The experimental results show that the proposed method has promising results that able to replace the missing data as well as achieves high accuracy rates and low root mean square error values.

关键词： missing values imputation fuzzy clustering mean majority vote microarray data

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A study on the relevance of feature selection methods in microarray data

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Open Bioinformatics Journal 2018年第1期11卷 117-139页

作者： Sahu, Barnali Dehuri, Satchidananda Jagadev, Alok Department of Computer Science ITER Siksha ‘O’ Anusandhan University Bhubaneswar India Department of Information and Communication Technology Fakirmohan University Vyasa Vihar Balasore Odisha India School of Computer Engineering KIIT University Bhubaneswar Odisha India

Background: This paper studies the relevance of feature selection algorithms in microarray data for effective analysis. With no loss of generality, we present a list of feature selection algorithms and propose a generic categorizing framework that systematically groups algorithms into categories. The generic categorizing framework is based on search strategies and evaluation criteria. Further, it provides guidelines for selecting feature selection algorithms in general and in specific to the context of this study. In the context of microarray data analysis, the feature selection algorithms are classified into soft and non-soft computing categories. Their performance analysis with respect to microarray data analysis has been presented. Conclusion: We summarize this study by highlighting pointers to recent trends and challenges of feature selection research and development in microarray data. © 2018 Sahu et al.

关键词： data mining data mining tasks Feature selection microarray data Nonsoft computing technique Search strategies Soft computing technique

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CFSES optimization Feature Selection with neural network classification for microarray data analysis 2

CFSES optimization Feature Selection with neural network cla...

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2nd International Conference on data Science and Business Analytics (ICDSBA)

作者： Patra, Bichitrananda Bisoyi, Sudhansu Sekhar Siksha O Anusandhan Deemed Be Univ Dept Comp Sci & Engn ITER Bhubaneswar Odisha India KL Univ Dept CSE Vaddeswaram Guntur India

ISBN: (纸本)9781538684313

The DNA microarray data are enabling to measure the countenance levels of genes simultaneously, so long as an excessive for tuitous for cancer prediction. The number of features or genes is often more than thousands but in another side, the number of instances or subjects is less than hundreds. For that, it is required and vital to perform gene selection for classification. Feature selection moderates this problem by eradicating irrelevant genes or features from data. So it optimizes the computational cost with concentrated data. At here, we propose a new methodology for feature selection to determine marker genes that are relevant to a type of cancer. A different bio-stimulated procedure entitled as CFSES (Correlation-based Feature Selection Elephant Search) is projected in this work. We use the Linear Support Vector Machine (Linear-SVNI), SMO-SVNI and NILP to make classification with use of the selected marker genes. The performance of marker gene selection and classification are illustrated in two real data analysis.

关键词： Feature reduction Correlation-based Feature Selection (CF) Elephant search Algorithm (ESA) Multilayer Perceptron (MLP) SVM microarray data

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Boosting Support Vector Machines for Imbalanced microarray data 3

Boosting Support Vector Machines for Imbalanced Microarray D...

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3rd INNS Conference on Big data and Deep Learning (INNS BDDL)

作者： Pratama, Risky Frasetio Wahyu Purnami, Santi Wulan Rahayu, Santi Puteri Inst Teknol Sepuluh Nopember Dept Stat Sukolilo 60111 Surabaya 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. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： microarray data Imbalanced Class SVM SMOTE AdaBoost SMOTEBoost

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A New ECOC Algorithm for Multiclass microarray data Classification 24

A New ECOC Algorithm for Multiclass Microarray Data Classifi...

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24th International Conference on Pattern Recognition (ICPR)

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

ISBN: (纸本)9781538637883

The classification of multi-class microarray datasets is a hard task because of the small samples size in each class and the heavy overlaps among classes. To effectively solve these problems, we propose a novel Error Correcting Output Code (ECOC) algorithm by Enhance Class Separability related data Complexity measures during encoding process, named as ECOCECS. In this algorithm, two nearest neighbor related DC measures are deployed to extract the intrinsic overlapping information from microarray data. Our ECOC algorithm aims to search an optimal class split scheme by minimizing these measures. The class splitting process ends when each class is separated from others, and then the class assignment scheme is mapped as a coding matrix. Experiments are carried out on seven microarray datasets, and results demonstrate the effectiveness and robustness of our method in comparison with four state-ofart ECOC methods. In short, our work shows that it is promising to apply the DC theory to ECOC framework.

关键词： ECOC Multi-class data Complexity microarray data

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Robust gene selection methods using weighting schemes for microarray data analysis

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BMC BIOINFORMATICS 2017年第1期18卷 1-15页

作者： Kang, Suyeon Song, Jongwoo Ewha Womans Univ Dept Stat Seoul South Korea

Background: A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. Results: We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. Conclusions: The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

关键词： microarray data Gene selection method Significance analysis of microarrays Noisy data Robustness False discovery rate

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microarray data Analysis of Yeast data using Sparse Non-Negative Matrix Factorization

Microarray Data Analysis of Yeast Data using Sparse Non-Nega...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Passi, Kalpdrum Draper, Paul Santala, Jillana Jain, Chakresh Kumar Laurentian Univ Dept Math & Comp Sci Sudbury ON Canada Jaypee Inst Informat Technol Dept Biotechnol Noida India

ISBN: (纸本)9781538626528

microarray expression data contains observations from thousands of genes across hundreds of samples. To extract meaningful information from these large datasets, the dimensionality reduction technique known as non-negative matrix factorization, or NMF, is introduced. This tool transforms the data and makes it more amenable to clustering. NMF was applied to a yeast microarray dataset. Three main clusters were discovered, corresponding to three distinct metabolic cycles. The data were also clustered using the k-means algorithm, and the clustering result was highly similar to that obtained by NMF.

关键词： sparse non-negative matrix factorization yeast microarray data k-means

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Using a grid computing-based meta-evolutionary mining approach for the microarray data cancer-categorization

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ENGINEERING COMPUTATIONS 2017年第1期34卷 134-144页

作者： Chiang, Tai-Wei Chen, Ta-Cheng Natl Formosa Univ Dept Informat Management Yunlin Taiwan Asia Univ Dept M Commerce & Multimedia Applicat Taichung Taiwan

Purpose - The categorization response model through gene expression patterns turns into one of the most favorable utilizations of the microarray technology. In this study, the aim is to propose a grid computing-based meta-evolutionary mining approach as a categorization response model for gene selection and cancer classification. Design/methodology/approach - The proposed approach is based on the grid computing infrastructure for establishing the best attributes set selected from a big microarray data. The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy. Findings - Examples for several benchmarking cancer microarray data sets were used to evaluate the proposed approach, whose results are also compared with other approaches in literatures. Experimental results from four benchmarking problems indicate that the proposed approach works effectively and efficiently, and the results of the proposed methods are superior to or as well as other existing methods in literatures. Originality/value - The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach to discover the best feature subset automatically from the microarray tumor database. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.

关键词： Cancer categorization Grid computing Meta-evolutionary approach microarray data

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Prediction of NSCLC recurrence from microarray data with GEP

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IET SYSTEMS BIOLOGY 2017年第3期11卷 77-86页

作者： Al-Anni, Russul Hou, Jingyu Abdu-aljabar, Rana Dhia'a Xiang, Yong Deakin Univ Sch Informat Technol Geelong Vic Australia Al Nahrain Univ Informat Engn Coll Baghdad Iraq

Lung cancer is one of the deadliest diseases in the world. Non-small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.

关键词： lung cancer lab-on-a-chip genetics patient diagnosis NSCLC recurrence prediction microarray data GE programming nonsmall cell lung cancer cancer recurrence histopathology data microarray gene expression prognostic genes

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A reductionist approach to extract robust molecular markers from microarray data series - Isolating markers to track osseointegration

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JOURNAL OF BIOMEDICAL INFORMATICS 2017年 68卷 104-111页

作者： Barik, Anwesha Banerjee, Satarupa Dhara, Santanu Chakravorty, Nishant Indian Inst Technol Sch Med Sci & Technol Kharagpur 721302 W Bengal India

Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in microarray data series and thereby extract robust molecular markers to track biological processes. The methodology has been illustrated by analyzing whole genome expression studies on bone-implant integration (ossointegration). Being a biological process, osseointegration is known to leave a trail of genetic footprint during the course. In spite of existence of enormous amount of raw data in public repositories, researchers still do not have access to a panel of genes that can definitively track osseointegration. The results from our study revealed panels comprising of matrix metalloproteinases and collagen genes were able to track osseointegration on implant surfaces (MMP9 and COL1A2 on micro-textured;MMP12 and COL6A3 on superimposed nano-textured surfaces) with 100% classification accuracy, specificity and sensitivity. Further, our analysis showed the importance of the progression of the duration in establishment of the mechanical connection at bone-implant surface. The findings from this study are expected to be useful to researchers investigating osseointegration of novel implant materials especially at the early stage. The methodology demonstrated can be easily adapted by scientists in different fields to analyze large databases for other biological processes. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Osseointegration microarray data Statistical classifiers Gene ontology Surface texture

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