Hybrid Feature Selection of Breast Cancer Gene Expression Microarray Data Based on Metaheuristic Methods: A Comprehensive Review

Ali, Nursabillilah Mohd; Besar, Rosli; Aziz, Nor Azlina Ab.

doi:10.3390/sym14101955

Cited by 10 publications

(9 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A range of algorithms have been used for multiclass gene expression classification, including (SVM), (ANN), decision trees, and (k-NN), and their performance has been compared in various studies [4]. Metaheuristics, such as genetic algorithms, particle swarm optimization, and artificial bee colonies, have also been utilized for feature selection in microarray gene expression data [6,7]. Hybrid methods, combining multiple techniques, have also been proposed and compared for feature selection and classification in microarray gene expression data [8][9][10].…”

Section: Significance Of Swarm Intelligence (Si)mentioning

confidence: 99%

Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

Gulande,

Awale

2024

CSSE

View full text Add to dashboard Cite

The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm algorithm, fusing the capabilities of the Artificial Bee Colony (ABC) and Firefly algorithms, aimed at improving feature selection in gene expression classification. The proposed method undergoes rigorous validation through statistical machine learning techniques and quantitative parameter evaluation, with comprehensive comparisons to established techniques in the field. The findings underscore the superiority of the hybrid Swarm Intelligence approach for feature selection in gene expression classification, offering promising prospects for enhancing cancer diagnosis and prognosis.

show abstract

Section: Significance Of Swarm Intelligence (Si)mentioning

confidence: 99%

Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

Gulande,

Awale

2024

CSSE

View full text Add to dashboard Cite

show abstract

“…Therefore, the authors in the present study were not involved with animals or human participants. However, the relevant local Ethics Committee approved the original retrospective studies [34].…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…Moreover, the hybrid FS methods are sometimes more applicable than filter-based methods in screening gene biomarkers from the cancer microarray gene expression dataset [20]. The nature-inspired FS methods select the best optimal feature subset using heuristic search to maximize the classification accuracy in binary and multiclass classification problems [34]. Metaheuristics algorithms have also been used to solve many NP-hard problems in various fields, such as function optimization [35][36][37], feature extraction for the image-based classification of cancer [38], feature selection for cancer diagnosis [39,40], and biomedical engineering [41][42][43][44] and circuit design [45,46].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

2023

View full text Add to dashboard Cite

The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

show abstract

“…It considered six key perspectives: methods employed, classifiers used, datasets utilized, range of dataset dimensions, performance metrics evaluated, and the results achieved. A comprehensive overview of hybrid feature selection techniques for analyzing gene expression microarray data in breast cancer was proposed by Mohd et al 31 . Their work focused more on combining metaheuristic algorithms with feature selection methods to identify the most informative and relevant genes for breast cancer classification.…”

Section: Related Workmentioning

confidence: 99%

A novel feature selection algorithm for identifying hub genes in lung cancer

Mohamed,

Ezugwu,

Fonou-Dombeu

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Lung cancer, a life-threatening disease primarily affecting lung tissue, remains a significant contributor to mortality in both developed and developing nations. Accurate biomarker identification is imperative for effective cancer diagnosis and therapeutic strategies. This study introduces the Voting-Based Enhanced Binary Ebola Optimization Search Algorithm (VBEOSA), an innovative ensemble-based approach combining binary optimization and the Ebola optimization search algorithm. VBEOSA harnesses the collective power of the state-of-the-art classification models through soft voting. Moreover, our research applies VBEOSA to an extensive lung cancer gene expression dataset obtained from TCGA, following essential preprocessing steps including outlier detection and removal, data normalization, and filtration. VBEOSA aids in feature selection, leading to the discovery of key hub genes closely associated with lung cancer, validated through comprehensive protein–protein interaction analysis. Notably, our investigation reveals ten significant hub genes—ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B—each demonstrating substantial involvement in the domain of lung cancer. Furthermore, our pathway analysis sheds light on the prominence of strategic pathways such as salivary secretion and the calcium signaling pathway, providing invaluable insights into the intricate molecular mechanisms underpinning lung cancer. We also utilize the weighted gene co-expression network analysis (WGCNA) method to identify gene modules exhibiting strong correlations with clinical attributes associated with lung cancer. Our findings underscore the efficacy of VBEOSA in feature selection and offer profound insights into the multifaceted molecular landscape of lung cancer. Finally, we are confident that this research has the potential to improve diagnostic capabilities and further enrich our understanding of the disease, thus setting the stage for future advancements in the clinical management of lung cancer. The VBEOSA source codes is publicly available at https://github.com/TEHNAN/VBEOSA-A-Novel-Feature-Selection-Algorithm-for-Identifying-hub-Genes-in-Lung-Cancer.

show abstract

Hybrid Feature Selection of Breast Cancer Gene Expression Microarray Data Based on Metaheuristic Methods: A Comprehensive Review

Cited by 10 publications

References 80 publications

Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

A novel feature selection algorithm for identifying hub genes in lung cancer

Contact Info

Product

Resources

About