Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm

Cancer remains a leading cause of death worldwide; the World Health Organization (WHO) reports that there have been nearly 10 million cancer-related deaths in recent years, with breast cancer affecting over 2.1 million women annually on a global scale, posing significant challenges for early detection and diagnosis. Gene selection, using DNA microarray data, is crucial for reducing the presence of less informative genes and ensuring the selection of genes relevant to disease diagnosis. Cancer classification involves identifying the type of cancer and determining the extent of tumor growth and spread. This research focuses on improving gene selection for cancer classification using the XGBoost classifier, an efficient open-source implementation of the gradient-boosted trees algorithm. The primary goal is to enhance the performance of gene selection, enabling timely and appropriate treatments for cancer patients, as early detection is vital for ensuring a full recovery. Additionally, this research aims to reduce the time and expense associated with gene selection for cancer classification while increasing classification accuracy. The proposed method achieved an accuracy of approximately 93%, with precision, recall, and F1-score values of 93%, 87%, and 90%, respectively. The study highlights the potential of the XGBoost classifier in optimizing gene selection and improving diagnostic processes. Future work will focus on enhancing the accuracy of gene selection for cancer classification and reducing the number of irrelevant genes before proceeding to subsequent processes. This approach holds promises for streamlining the diagnostic process, improving patient outcomes, and offering significant benefits in timely cancer treatment.

BackgroundMicroarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in microarray data, which generally contain a large number of genes and have a small number of samples. In recent years, various approaches have been developed for gene selection of microarray data. Generally, they are divided into three categories: filter, wrapper and embedded methods. Regularization methods are an important embedded technique and perform both continuous shrinkage and automatic gene selection simultaneously. Recently, there is growing interest in applying the regularization techniques in gene selection. The popular regularization technique is Lasso (L1), and many L1 type regularization terms have been proposed in the recent years. Theoretically, the Lq type regularization with the lower value of q would lead to better solutions with more sparsity. Moreover, the L1/2 regularization can be taken as a representative of Lq (0 <q < 1) regularizations and has been demonstrated many attractive properties.ResultsIn this work, we investigate a sparse logistic regression with the L1/2 penalty for gene selection in cancer classification problems, and propose a coordinate descent algorithm with a new univariate half thresholding operator to solve the L1/2 penalized logistic regression. Experimental results on artificial and microarray data demonstrate the effectiveness of our proposed approach compared with other regularization methods. Especially, for 4 publicly available gene expression datasets, the L1/2 regularization method achieved its success using only about 2 to 14 predictors (genes), compared to about 6 to 38 genes for ordinary L1 and elastic net regularization approaches.ConclusionsFrom our evaluations, it is clear that the sparse logistic regression with the L1/2 penalty achieves higher classification accuracy than those of ordinary L1 and elastic net regularization approaches, while fewer but informative genes are selected. This is an important consideration for screening and diagnostic applications, where the goal is often to develop an accurate test using as few features as possible in order to control cost. Therefore, the sparse logistic regression with the L1/2 penalty is effective technique for gene selection in real classification problems.

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.

Objective:Cancer diagnosis is one of the most vital emerging clinical applications of microarray data. Due to the high dimensionality, gene selection is an important step for improving expression data classification performance. There is therefore a need for effective methods to select informative genes for prediction and diagnosis of cancer. The main objective of this research was to derive a heuristic approach to select highly informative genes.Methods:A metaheuristic approach with a Genetic Algorithm with Levy Flight (GA-LV) was applied for classification of cancer genes in microarrays. The experimental results were analyzed with five major cancer gene expression benchmark datasets.Result:GA-LV proved superior to GA and statistical approaches, with 100% accuracy for the dataset for Leukemia, Lung and Lymphoma. For Prostate and Colon datasets the GA-LV was 99.5% and 99.2% accurate, respectively.Conclusion:The experimental results show that the proposed approach is suitable for effective gene selection with all benchmark datasets, removing irrelevant and redundant genes to improve classification accuracy.

Feature selection is a commonly addressed problem in classification. In gene expression-based cancer classification, a large number of genes in conjunction with a small number of samples makes the gene selection problem more important but also more challenging. Support vector machine as a popular classification algorithm, has been successfully used in SVM-RFE method for gene selection. This paper proposes a variant of SVM-RFE to do gene selection for cancer classification with expression data. Multiple support vector machine classifiers from a leave-one-out procedure are used to compute the feature ranking scores. The numerical experiments also show the good and stable performance of the proposed method.

These days, the classification between normal and cancerous tissues and between different types of cancers represents a very important issue. Selecting the little informative number of genes is considered the main challenge in the cancer diagnosis issue. Therefore, Gene selection is usually the preliminary step for solving the cancer classification problems. Bio-inspired metaheuristic optimization algorithms, when used to solve gene selection and classification problems, they demonstrate their effectiveness. Barnacles Mating Optimizer (BMO) algorithm, which imitates the behavior of mating barnacles in nature for solving optimization problems, is considered one of these algorithms. In this paper, Barnacles Mating Optimizer (BMO) algorithm augmented with Support Vector Machines (SVM) called BMO-SVM is proposed for a microarray gene expression profiling in order to select the most predictive and informative genes for cancer classification. Conducting a comparative experimental study among a set of the most common bio-inspired optimization techniques to specify the most effective. A binary microarray dataset (i.e., leukemia1) and a multi-class microarray dataset (i.e., SRBCT, lymphoma, and leukemia2) are used for testing the performance of the proposed model. The experimental results revealed the superiority of the proposed BMO-SVM approach against several well-known meta-heuristic optimization algorithms, such as the Tunicate Swarm Algorithm (TSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). It is worth mentioning that our proposed algorithm achieves a high informational superiority percentage compared to other algorithms.

Feature selection has become the focus of many real-world application oriented developments and applied research in recent years. With the rapid advancement of computer and database technologies, problems "with hundreds and thousands of variables or features are now ubiquitous in pattern recognition, data mining, and machine learning [1], [2]. In this article, we consider two real-world feature selection applications: gene selection in cancer classification based on microarray data and band selection for pixel classification using hyperspectral imagery data.

Gene selection is an important problem in microarray data processing. A new gene selection method based on Wilcoxon rank sum test and Support Vector Machine (SVM) is proposed in this paper. First, Wilcoxon rank sum test is used to select a subset. Then each selected gene is trained and tested using SVM classifier with linear kernel separately, and genes with high testing accuracy rates are chosen to form the final reduced gene subset. Leave-one-out cross validation (LOOCV) classification results on two datasets: Breast Cancer and ALL/AML leukemia, demonstrate the proposed method can get 100% success rate with final reduced subset. The selected genes are listed and their expression levels are sketched, which show that the selected genes can make clear separation between two classes.

DNA micro-arrays now permit scientists to screen thousands of genes simultaneously and determine whether those genes are active, hyperactive or silent in normal or cancerous tissue. Because these new micro-array devices generate bewildering amounts of raw data, new analytical methods must be developed to sort out whether cancer tissues have distinctive signatures of gene expression over normal tissues or other types of cancer tissues. In this paper, we address the problem of selection of a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays. Using available training examples from cancer and normal patients, we build a classifier suitable for genetic diagnosis, as well as drug discovery. Previous attempts to address this problem select genes with correlation techniques. We propose a new method of gene selection utilizing Support Vector Machine methods based on Recursive Feature Elimination (RFE). We demonstrate experimentally that the genes selected by our techniques yield better classification performance and are biologically relevant to cancer. In contrast with the baseline method, our method eliminates gene redundancy automatically and yields better and more compact gene subsets. In patients with leukemia our method discovered 2 genes that yield zero leave-one-out error, while 64 genes are necessary for the baseline method to get the best result (one leave-one-out error). In the colon cancer database, using only 4 genes our method is 98% accurate, while the baseline method is only 86% accurate.

Despite the considerable efforts that are done for Microarray gene expression profile, biomarker discovery or gene selection process remains a complex challenge for biologists and computer scientists. This article presented an Artificial Bee Colony (ABC) based approach for accurate classification of cancer microarray data using support vector machine (SVM) as a classifier. The approach is able to deal with the high dimensionality of the microarray data. According to literature for microarray data analysis, up to our knowledge, this is the first attempt to apply Artificial Bee Colony based algorithm as gene selection method for cancer classification problem using Microarray gene expression profile. Comparing with other algorithms that are proposed in the literature, we can conclude that our proposed algorithm (ABC-SVM) obtains promising results for gene selection and cancer classification problems.

Given the thousands of genes and the small number of samples, gene selection has emerged as an important research problem in microarray data analysis. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is one of a group of recently described algorithms which represent the stat-of-the-art for gene selection. Just like SVM itself, SVM-RFE was originally designed to solve binary gene selection problems. Several groups have extended SVM-RFE to solve multiclass problems using one-versus-all techniques. However, the genes selected from one binary gene selection problem may reduce the classification performance in other binary problems. In the present study, we propose a family of four extensions to SVM-RFE (called MSVM-RFE) to solve the multiclass gene selection problem, based on different frameworks of multiclass SVMs. By simultaneously considering all classes during the gene selection stages, our proposed extensions identify genes leading to more accurate classification.

BackgroundMicroarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. The multi-class SVM algorithm of Crammer and Singer provides a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. In this paper, we propose to improve the procedure by imposing shrinkage penalties in learning to enforce solution sparsity.ResultsThe original multi-class SVM of Crammer and Singer is effective for multi-class classification but does not conduct variable selection. We improved the method by introducing soft-thresholding type penalties to incorporate variable selection into multi-class classification for high dimensional data. The new methods were applied to simulated data and two cancer gene expression data sets. The results demonstrate that the new methods can select a small number of genes for building accurate multi-class classification rules. Furthermore, the important genes selected by the methods overlap significantly, suggesting general agreement among different variable selection schemes.ConclusionsHigh accuracy and sparsity make the new methods attractive for cancer diagnostics with gene expression data and defining targets of therapeutic intervention.Availability: The source MATLAB code are available from http://math.arizona.edu/~hzhang/software.html.

BackgroundGene selection is one of the critical steps in the course of the classification of microarray data. Since particle swarm optimization has no complicated evolutionary operators and fewer parameters need to be adjusted, it has been used increasingly as an effective technique for gene selection. Since particle swarm optimization is apt to converge to local minima which lead to premature convergence, some particle swarm optimization based gene selection methods may select non-optimal genes with high probability. To select predictive genes with low redundancy as well as not filtering out key genes is still a challenge.ResultsTo obtain predictive genes with lower redundancy as well as overcome the deficiencies of traditional particle swarm optimization based gene selection methods, a hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization is proposed in this paper. To select the genes highly related to out samples’ classes, a gene scoring strategy based on randomization and extreme learning machine is proposed to filter much irrelevant genes. With the third-level gene pool established by multiple filter strategy, an improved particle swarm optimization is proposed to perform gene selection. In the improved particle swarm optimization, to decrease the likelihood of the premature of the swarm the Metropolis criterion of simulated annealing algorithm is introduced to update the particles, and the half of the swarm are reinitialized when the swarm is trapped into local minima.ConclusionsCombining the gene scoring strategy with the improved particle swarm optimization, the new method could select functional gene subsets which are significantly sensitive to the samples’ classes. With the few discriminative genes selected by the proposed method, extreme learning machine and support vector machine classifiers achieve much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

In bioinformatics, medical diagnosis models might be significantly impacted by high-dimensional data generated by high-throughput technologies. This data includes redundant or irrelevant genes making it challenging to identify the relevant genes from such high-dimensional data. Therefore, an effective feature selection (FS) technique can significantly reduce the degree of dimensionality to enhance the performance and accuracy of medical diagnosis. Cuckoo Search Algorithm (CSA) is applied for gene selection and found to be effective in terms of exploitation, exploration, and convergence. However, most of the current CSA-based FS techniques deal with gene selection problem as a single objective rather than a multi-objective mechanism. This article proposes a Multi-Objective Binary Cuckoo Search Algorithm (MOBCSA) for gene selection. The MOBCSA extends the standard CSA considering multiple objectives such as accuracy of classification and number of selected genes. MOBCSA utilizes S-shaped transfer function for transforming the algorithm’s search space from a continuous to a binary search space. MOBCSA integrates two components: an external archive to save the pareto optimal solutions attained during the search process and an adaptive crowding distance updating mechanism integrated into the archive to maintain diversity and increase the coverage of optimal solutions. To evaluate the performance of MOBCSA, the evaluation experiments were conducted on six benchmark biomedical datasets using three different classifiers. Then, the obtained experimental results were compared against four multi-objective-based state of the art FS methods. The findings prove that MOBCSA surpasses the other methods in both accuracy of classification and number of selected genes, where it has obtained an average accuracy ranging from 92.79% to 98.42% and an average number of selected genes ranging from 15.67 to 27.88 for different classifiers and datasets.

Improving PLS–RFE based gene selection for microarray data classification