scholarly journals Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

2021 ◽  
Vol 7 ◽  
pp. e562
Author(s):  
Muhammad Hamraz ◽  
Naz Gul ◽  
Mushtaq Raza ◽  
Dost Muhammad Khan ◽  
Umair Khalil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Brier Score ◽  
Classification Error ◽  
Support Vector ◽  
Microarray Gene Expression ◽  
Absolute Deviation

In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD). This method robustly identifies the most discriminative genes by considering the overlapping scores of the gene expression values for binary class problems. Genes with a high degree of overlap between classes are discarded and the ones that discriminate between the classes are selected. The results of the proposed method are compared with five state-of-the-art gene selection methods based on classification error, Brier score, and sensitivity, by considering eleven gene expression datasets. Classification of observations for different sets of selected genes by the proposed method is carried out by three different classifiers, i.e., random forest, k-nearest neighbors (k-NN), and support vector machine (SVM). Box-plots and stability scores of the results are also shown in this paper. The results reveal that in most of the cases the proposed method outperforms the other methods.

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

scholarly journals mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Hala Alshamlan ◽  
Ghada Badr ◽  
Yousef Alohali
Keyword(s):  
Gene Expression ◽  
Gene Selection ◽  
Cancer Classification ◽  
Support Vector ◽  
Optimization Approach ◽  
Selection Algorithm ◽  
Classification Problems ◽  
Microarray Gene Expression ◽  
Abc Algorithm ◽  
Microarray Gene

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

scholarly journals Microarray Gene Expression Data Classification using a Hybrid Algorithm: MRMRAGA

2019 ◽  
Vol 8 (10) ◽  
pp. 706-713
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Feature Selection Method ◽  
Small Sample ◽  
Adaptive Genetic Algorithm ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Microarray Gene

In the field of microarray gene expression research, the high dimension of the features with a comparatively small sample size of these data became necessary for the development of a robust and efficient feature selection method in order to perform classification task more precisely on gene expression data. We propose the hybrid feature selection (mRMRAGA) approach in this paper, which combines the minimum redundancy and maximum relevance (mRMR) with the adaptive genetic algorithm (AGA). The mRMR method is frequently used to identify the characteristics more accurately for gene and its phenotypes. Then their relevance is narrowed down which is described in pairing with its relevant feature selection. This approach is known as Minimum Redundancy and Maximum Relevance. The Genetic Algorithm (GA) has been propelled with the procedure of natural selection and it is based on heuristic search method. And the adaptive genetic algorithm is improvised one which gives better performance. We have conducted an experiment on four benchmarked dataset using our proposed approach and then classified using four well-known classification approaches. The accuracy was measured and observed that it gives better performance compared to the other conventional feature selection methods.

Correlation-based Gene Selection and Classification Using Taguchi-BPSO

2010 ◽  
Vol 49 (03) ◽  
pp. 254-268 ◽  
Author(s):  
C.-S. Yang ◽  
K.-C. Wu ◽  
C.-H. Yang ◽  
L.-Y. Chuang
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Microarray Data ◽  
Error Rate ◽  
Gene Expression Analysis ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Classification Error ◽  
Classification Error Rate

Summary Background: Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. Objective: The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Method: In this paper, correlation-based feature selection (CFS) and Taguchi-binary particle swarm optimization (TBPSO) were combined into a hybrid method, and the K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Results: Experimental results show that this hybrid method effectively simplifies feature selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. Conclusion: The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

scholarly journals Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Selen Yılmaz Isıkhan ◽  
Erdem Karabulut ◽  
Celal Reha Alpar
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Feature Selection Method ◽  
Microarray Gene Expression Data ◽  
Cutoff Point ◽  
Gradient Boosting ◽  
Support Vector ◽  
Microarray Gene Expression ◽  
Dose Prediction ◽  
Clinical Dose

Background/Aim. Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods. Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results. The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion. Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n=25 as a cutoff point for RT bagging to outperform a single RT.

scholarly journals A COMPARATIVE STUDY ON GENE SELECTION METHODS FOR TISSUES CLASSIFICATION ON LARGE SCALE GENE EXPRESSION DATA

2016 ◽  
Vol 78 (5-10) ◽  
Author(s):  
Farzana Kabir Ahmad
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Large Scale ◽  
Gene Selection ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Expression Data ◽  
Selection Methods ◽  
Normal Tissues

Deoxyribonucleic acid (DNA) microarray technology is the recent invention that provided colossal opportunities to measure a large scale of gene expressions simultaneously. However, interpreting large scale of gene expression data remain a challenging issue due to their innate nature of “high dimensional low sample size”. Microarray data mainly involved thousands of genes, n in a very small size sample, p which complicates the data analysis process. For such a reason, feature selection methods also known as gene selection methods have become apparently need to select significant genes that present the maximum discriminative power between cancerous and normal tissues. Feature selection methods can be structured into three basic factions; a) filter methods; b) wrapper methods and c) embedded methods. Among these methods, filter gene selection methods provide easy way to calculate the informative genes and can simplify reduce the large scale microarray datasets. Although filter based gene selection techniques have been commonly used in analyzing microarray dataset, these techniques have been tested separately in different studies. Therefore, this study aims to investigate and compare the effectiveness of these four popular filter gene selection methods namely Signal-to-Noise ratio (SNR), Fisher Criterion (FC), Information Gain (IG) and t-Test in selecting informative genes that can distinguish cancer and normal tissues. In this experiment, common classifiers, Support Vector Machine (SVM) is used to train the selected genes. These gene selection methods are tested on three large scales of gene expression datasets, namely breast cancer dataset, colon dataset, and lung dataset. This study has discovered that IG and SNR are more suitable to be used with SVM. Furthermore, this study has shown SVM performance remained moderately unaffected unless a very small size of genes was selected.

scholarly journals Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set

2020 ◽  
Vol 30 (1) ◽  
pp. 130-141
Author(s):  
Shemim Begum ◽  
Ram Sarkar ◽  
Debasis Chakraborty ◽  
Ujjwal Maulik
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Rough Set ◽  
Early Stage ◽  
Feature Selection Method ◽  
Support Vector ◽  
Global Consistency ◽  
Learning Technique ◽  
Human Death ◽  
Fuzzy Preference

Abstract Cancer is fast becoming an alarming cause of human death. However, it has been reported that if the disease is detected at an early stage, diagnosed, treated appropriately, the patient has better chances of survival long life. Machine learning technique with feature-selection contributes greatly to the detecting of cancer, because an efficient feature-selection method can remove redundant features. In this paper, a Fuzzy Preference-Based Rough Set (FPRS) blended with Support Vector Machine (SVM) has been applied in order to predict cancer biomarkers for biological and gene expression datasets. Biomarkers are determined by deploying three models of FPRS, namely, Fuzzy Upward Consistency (FUC), Fuzzy Downward Consistency (FLC), and Fuzzy Global Consistency (FGC). The efficiency of the three models with SVM on five datasets is exhibited, and the biomarkers that have been identified from FUC models have been reported.

scholarly journals A Two-Stage Method Based on Multiobjective Differential Evolution for Gene Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shuangbao Song ◽  
Xingqian Chen ◽  
Zheng Tang ◽  
Yuki Todo
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Multiobjective Optimization ◽  
Differential Evolution ◽  
Gene Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Selection Problem ◽  
Objective Functions ◽  
Two Stage

Microarray gene expression data provide a prospective way to diagnose disease and classify cancer. However, in bioinformatics, the gene selection problem, i.e., how to select the most informative genes from thousands of genes, remains challenging. This problem is a specific feature selection problem with high-dimensional features and small sample sizes. In this paper, a two-stage method combining a filter feature selection method and a wrapper feature selection method is proposed to solve the gene selection problem. In contrast to common methods, the proposed method models the gene selection problem as a multiobjective optimization problem. Both stages employ the same multiobjective differential evolution (MODE) as the search strategy but incorporate different objective functions. The three objective functions of the filter method are mainly based on mutual information. The two objective functions of the wrapper method are the number of selected features and the classification error of a naive Bayes (NB) classifier. Finally, the performance of the proposed method is tested and analyzed on six benchmark gene expression datasets. The experimental results verified that this paper provides a novel and effective way to solve the gene selection problem by applying a multiobjective optimization algorithm.

scholarly journals Regulatory genes identification within functional genomics experiments for tissue classification into binary classes via machine learning techniques

2020 ◽  
pp. 1-21
Author(s):  
Bushra Wazir ◽  
Dost Muhammad Khan ◽  
Umair Khalil ◽  
Muhammad Hamraz ◽  
Naz Gul ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Classification Accuracy ◽  
Gene Selection ◽  
Machine Learning Techniques ◽  
Brier Score ◽  
Support Vector ◽  
Target Tissue ◽  
Selection Methods ◽  
Microarray Gene Expression

Abstract Objectives: The aim of this study is to filter out the most informative genes that mainly regulate the target tissue class, increase classification accuracy, reduce the curse of dimensionality,  and discard redundant and irrelevant genes. Methods: This paper presented the idea of gene selection using bagging sub-forest (BSF). The proposed method provided genes importance grounded on the idea specified in the standard random forest algorithm. The new method is compared with three state-of-the art methods, i.e., Wilcoxon, masked painter and proportional overlapped score (POS). These methods were applied on 5 data sets, i.e. Colon, Lymph node breast cancer, Leukemia, Serrated colorectal carcinomas, and Breast Cancer. Comparison was done by selecting top 20 genes by applying the gene selection methods and applying random forest (RF) and support vector machine (SVM) classifiers to assess their predictive performance on the datasets with selected genes. Classification accuracy, Brier score, and sensitivity have been used as performance measures. Results: The proposed method gave better results than the other methods using both random forest and SVM classifiers on all the datasets among all the feature selection methods. Conclusion: The proposed method showed improved performance in terms of classification accuracy, Brier score and sensitivity, and hence, could be used as a novel method for gene selection to classify tissue samples into their correct classes. Key Words: Gene selection, classification, random forest, cancer, microarray gene expression.

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