scholarly journals Gene Correlation Guided Gene Selection for Microarray Data Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dong Yang ◽  
Xuchang Zhu
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Effective Means ◽  
Cluster Structure ◽  
Coefficient Matrix ◽  
Cancer Data ◽  
Redundant Genes ◽  
Gene Correlation ◽  
High Gene ◽  
Microarray Datasets

The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.

scholarly journals Gene selection ensembles and classifier ensembles for medical diagnosis

2019 ◽  
Vol 56 (2) ◽  
pp. 117-138
Author(s):  
Małgorzata Ćwiklińska-Jurkowska
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Data Sets ◽  
Selection Methods ◽  
Classifier Ensembles ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Cancer Data ◽  
Misclassification Rates ◽  
Selection Of

SummaryThe usefulness of combining methods is examined using the example of microarray cancer data sets, where expression levels of huge numbers of genes are reported. Problems of discrimination into two groups are examined on three data sets relating to the expression of huge numbers of genes. For the three examined microarray data sets, the cross-validation errors evaluated on the remaining half of the whole data set, not used earlier for the selection of genes, were used as measures of classifier performance. Common single procedures for the selection of genes—Prediction Analysis of Microarrays (PAM) and Significance Analysis of Microarrays (SAM)—were compared with the fusion of eight selection procedures, or of a smaller subset of five of them, excluding SAM or PAM. Merging five or eight selection methods gave similar results. Based on the misclassification rates for the three examined microarray data sets, for any examined ensemble of classifiers, the combining of gene selection methods was not superior to single PAM or SAM selection for two of the examined data sets. Additionally, the procedure of heterogeneous combining of five base classifiers—k-nearest neighbors, SVM linear and SVM radial with parameter c=1, shrunken centroids regularized classifier (SCRDA) and nearest mean classifier—proved to significantly outperform resampling classifiers such as bagging decision trees. Heterogeneously combined classifiers also outperformed double bagging for some ranges of gene numbers and data sets, but merging is generally not superior to random forests. The preliminary step of combining gene rankings was generally not essential for the performance for either heterogeneously or homogeneously combined classifiers.

scholarly journals PLS Dimension Reduction for Classification with Microarray Data

2004 ◽  
Vol 3 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Anne-Laure Boulesteix
Keyword(s):  
Dimension Reduction ◽  
Microarray Data ◽  
Gene Selection ◽  
Simple Procedure ◽  
Binary Classification ◽  
Real Data ◽  
Good Prediction ◽  
Linear Discriminant ◽  
Cancer Data ◽  
Good Prediction Accuracy

Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new components is compared with some of the best state-of-the-art classification methods. Moreover, a boosting algorithm is applied to this classification method. In addition, a simple procedure to choose the number of PLS components is suggested. The connection between PLS dimension reduction and gene selection is examined and a property of the first PLS component for binary classification is proved. In addition, we show how PLS can be used for data visualization using real data. The whole study is based on 9 real microarray cancer data sets.

scholarly journals Swarm Intelligence Algorithms in Gene Selection Profile Based on Classification of Microarray Data: A Review

2021 ◽  
Vol 2 (01) ◽  
pp. 01-09
Author(s):  
Alan Jahwar ◽  
Nawzat Ahmed
Keyword(s):  
Swarm Intelligence ◽  
Microarray Data ◽  
Gene Selection ◽  
Classification Algorithms ◽  
Data Sets ◽  
Paper Briefly ◽  
Large Gene ◽  
Microarray Datasets ◽  
Selection Of

Microarray data plays a major role in diagnosing and treating cancer. In several microarray data sets, many gene fragments are not associated with the target diseases. A solution to the gene selection problem might become important when analyzing large gene datasets. The key task is to better represent genes through optimum accuracy in classifying the samples. Different gene classification algorithms have been provided in past studies; after all, they suffered due to the selection of several genes mostly in high-dimensional microarray data. This paper aims to review classification and feature selection with different microarray datasets focused on swarm intelligence algorithms. We explain microarray data and its types in this paper briefly. Moreover, our paper presents an introduction to most common swarm intelligence algorithms. A review on swarm intelligence algorithms in gene selection profile based on classification of Microarray Data is presented in this paper.

scholarly journals Incremental Search for Informative Gene Selection in Cancer Classification

2021 ◽  
Vol 5 (2) ◽  
pp. 15-21
Author(s):  
Fathima Fajila ◽  
Yuhanis Yusof
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Gene Selection ◽  
Subset Selection ◽  
Cancer Classification ◽  
Microarray Data Analysis ◽  
Informative Gene ◽  
Incremental Search ◽  
Selection Approach ◽  
Microarray Datasets

Although numerous methods of using microarray data analysis for classification have been reported, there is space in the field of cancer classification for new inventions in terms of informative gene selection. This study introduces a new incremental search-based gene selection approach for cancer classification. The strength of wrappers in determining relevant genes in a gene pool can be increased as they evaluate each possible gene’s subset. Nevertheless, the searching algorithms play a major role in gene’s subset selection. Hence, there is the possibility of finding more informative genes with incremental application. Thus, we introduce an approach which utilizes two searching algorithms in gene’s subset selection. The approach was efficient enough to classify five out of six microarray datasets with 100% accuracy using only a few biomarkers while the rest classified with only one misclassification.

A Review of Gene Selection Tools in Classifying Cancer Microarray Data

2017 ◽  
Vol 12 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Tham W. Shi ◽  
Wong S. Kah ◽  
Mohd S. Mohamad ◽  
Kohbalan Moorthy ◽  
Safaai Deris ◽  
...  
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Selection Tools

scholarly journals Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

2018 ◽  
Vol 14 (6) ◽  
pp. 868-880 ◽  
Author(s):  
Shilan S. Hameed ◽  
Fahmi F. Muhammad ◽  
Rohayanti Hassan ◽  
Faisal Saeed
Keyword(s):  
Gene Selection ◽  
Hybrid Approach ◽  
Microarray Datasets

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

scholarly journals Random forest for gene selection and microarray data classification

2011 ◽  
Vol 7 (3) ◽  
pp. 142-146 ◽  
Author(s):  
Kohbalan Moorthy ◽  
Mohd Saberi Mohamad
Keyword(s):  
Random Forest ◽  
Microarray Data ◽  
Gene Selection ◽  
Data Classification
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