scholarly journals Biogeography-based informative gene selection and cancer classification using SVM and Random Forests

2012 ◽  
Author(s):  
Sarvesh Nikumbh ◽  
Shameek Ghosh ◽  
V. K. Jayaraman
Keyword(s):  
Random Forests ◽  
Gene Selection ◽  
Cancer Classification ◽  
Informative Gene

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.

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.

scholarly journals Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1936
Author(s):  
Abdulqader M. Almars ◽  
Majed Alwateer ◽  
Mohammed Qaraad ◽  
Souad Amjad ◽  
Hanaa Fathi ◽  
...  
Keyword(s):  
Gene Selection ◽  
Ensemble Methods ◽  
Cancer Classification ◽  
Biological Information ◽  
Gradient Boosting ◽  
New Approach ◽  
Biological Studies ◽  
Abnormal Cells ◽  
Gene Selection Algorithm ◽  
Gene Expression Levels

The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In most of the examined studies, cancer classification (Malignant and benign) accuracy was examined without disclosing biological information related to the classification process. A new approach was proposed to bridge the gap between cancer classification and the interpretation of the biological studies of the genes implicated in cancer. This study aims to develop a new hybrid model for cancer classification (by using feature selection mRMRe as a key step to improve the performance of classification methods and a distributed hyperparameter optimization for gradient boosting ensemble methods). To evaluate the proposed method, NB, RF, and SVM classifiers have been chosen. In terms of the AUC, sensitivity, and specificity, the optimized CatBoost classifier performed better than the optimized XGBoost in cross-validation 5, 6, 8, and 10. With an accuracy of 0.91±0.12, the optimized CatBoost classifier is more accurate than the CatBoost classifier without optimization, which is 0.81± 0.24. By using hybrid algorithms, SVM, RF, and NB automatically become more accurate. Furthermore, in terms of accuracy, SVM and RF (0.97±0.08) achieve equivalent and higher classification accuracy than NB (0.91±0.12). The findings of relevant biomedical studies confirm the findings of the selected genes.

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