Breast Cancer Classification With Microarray Gene Expression Data Based on Improved Whale Optimization Algorithm

Devi, S. Sathiya; Prithiviraj, K.

doi:10.4018/ijsir.317091

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…We utilized the R software to analyze the lung cancer gene expression data obtained from the Cancer Genome Atlas (TCGA) repository ( https://portal.gdc.cancer.gov/ ) The GDCquery function, available in the TCGAbiolinks library, was employed to query the data 38 – 41 . The lung cancer dataset consisted of 1208 clinical samples and 14,895 genes or features.…”

Section: Methodsmentioning

confidence: 99%

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

Mohamed,

Ezugwu,

Fonou-Dombeu

et al. 2023

Sci Rep

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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.

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Section: Methodsmentioning

confidence: 99%

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

Mohamed,

Ezugwu,

Fonou-Dombeu

et al. 2023

Sci Rep

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“…From their results, the proposed BMO-SVM approach performed better than the other well-known methods, such as Particle Swarm Optimization (PSO), the Tunicate Swarm Algorithm (TSA), Artificial Bee Colony (ABC), and Genetic Algorithm (GA). Devi et al 45 proposed an Improved Whale Optimization Algorithm (IWOA) algorithm for gene selection. The proposed solution used a multi-objective fitness function that balances error rate minimization and feature selection.…”

Section: Related Workmentioning

confidence: 99%

A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data

Mohamed,

Ezugwu,

Fonou-Dombeu

et al. 2023

Sci Rep

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Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy.

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“…Some scholars have studied the multi-feature fusion model of image classification using denoising convolutional neural networks and attention mechanisms (Zhang et al, 2023). The classification of breast cancer was based on the improved whale optimization algorithm and compared with other methods (Devi et al, 2023). Based on the interactive medical image segmentation framework of optimized swarm intelligence and convolutional neural networks, a method combining convolutional neural networks and swarm intelligence was proposed to optimally identify the required regions (Kaushal et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

Liu,

Xie,

et al. 2023

International Journal of Swarm Intelligence Research

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The diagnosis of pulmonary tuberculosis is a complicated process with a long wait. According to the WS 288-2017 standard, PTB can be divided into five types of imaging. To date, no relevant studies on PTB CT images based on the Yolov5 algorithm have been retrieved. To develop an improved strategy YOLOv5, for the classification of PTB lesions based on whole, CT slices were combined with three other modules. CT slices of PTB collected from hospitals were set as training, verification, and external test sets. It is compared with YOLOv5, SSD and RetinaNet neural network methods. The values of precision, recall, MAP, and F1-score of the improved strategy YOLOv5 for the external test were 0.707, 0.716, 0.715, and 0.71. In this study, based on the same dataset, the improved strategy YOLOv5 model has better results than other networks. Our method provides an effective method for the timely detection of PTB.

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Breast Cancer Classification With Microarray Gene Expression Data Based on Improved Whale Optimization Algorithm

Cited by 4 publications

References 18 publications

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

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

A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data

CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

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