scholarly journals The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

2014 ◽  
Vol 4 (3) ◽  
pp. 166-170 ◽  
Author(s):  
Hala M. Alshamlan ◽  
Ghada H. Badr ◽  
Yousef A. Alohali
Keyword(s):  
Gene Selection ◽  
Microarray Dataset ◽  
Cancer Classification ◽  
Selection Methods

scholarly journals Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Simin Hu ◽  
J. Sunil Rao
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Real Data ◽  
Cancer Classification ◽  
Simulation Studies ◽  
Selection Methods ◽  
Gene Subset ◽  
High Quality ◽  
Eigenvalue Ratio ◽  
Selection For

In gene selection for cancer classification using microarray data, we define an eigenvalue-ratio statistic to measure a gene's contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalue-ratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.

scholarly journals Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joe W. Chen ◽  
Joseph Dhahbi
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Biomarker Discovery ◽  
Lung Squamous Cell Carcinoma ◽  
Cancer Classification ◽  
Selection Methods ◽  
Biomarker Identification ◽  
Overlapping Method

AbstractLung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.

scholarly journals A Hybrid Barnacles Mating Optimizer Algorithm With Support Vector Machines for Gene Selection of Microarray Cancer Classification

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 64895-64905
Author(s):  
Essam H. Houssein ◽  
Diaa Salama Abdelminaam ◽  
Hager N. Hassan ◽  
Mustafa M. Al-Sayed ◽  
Emad Nabil
Keyword(s):  
Support Vector Machines ◽  
Gene Selection ◽  
Cancer Classification ◽  
Support Vector ◽  
Vector Machines ◽  
Selection Of

GENE SELECTION USING LOGISTIC REGRESSIONS BASED ON AIC, BIC AND MDL CRITERIA

2005 ◽  
Vol 01 (01) ◽  
pp. 129-145 ◽  
Author(s):  
XIAOBO ZHOU ◽  
XIAODONG WANG ◽  
EDWARD R. DOUGHERTY
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gene Selection ◽  
Information Criterion ◽  
Cancer Classification ◽  
Data Sets ◽  
Classification Methods ◽  
Gene Expressions ◽  
Experimental Conditions

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables (gene expressions) and the small number of experimental conditions. Many gene-selection and classification methods have been proposed; however most of these treat gene selection and classification separately, and not under the same model. We propose a Bayesian approach to gene selection using the logistic regression model. The Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the minimum description length (MDL) principle are used in constructing the posterior distribution of the chosen genes. The same logistic regression model is then used for cancer classification. Fast implementation issues for these methods are discussed. The proposed methods are tested on several data sets including those arising from hereditary breast cancer, small round blue-cell tumors, lymphoma, and acute leukemia. The experimental results indicate that the proposed methods show high classification accuracies on these data sets. Some robustness and sensitivity properties of the proposed methods are also discussed. Finally, mixing logistic-regression based gene selection with other classification methods and mixing logistic-regression-based classification with other gene-selection methods are considered.

scholarly journals Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model

2021 ◽  
Vol 29 ◽  
pp. 287-295
Author(s):  
Zhiming Zhou ◽  
Haihui Huang ◽  
Yong Liang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gene Selection ◽  
Simulated Data ◽  
Biological Data ◽  
Cancer Classification ◽  
High Dimensional ◽  
Data Set ◽  
Biomarker Selection

BACKGROUND: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection. OBJECTIVE: The aim of this paper is to give the model efficient gene selection capability. METHODS: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification. RESULTS: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods. CONCLUSIONS: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.

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.

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