ArticleJournal of medical systems2024
Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm.
Article in Journal of medical systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- GenAI-Supported Virtual Patients in Health Care Education: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Feature selection methodology based on explainable AI: application in predicting extubation success in the intensive care unit.Scientific reports · 2026Article
- Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis.Scientific reports · 2026Article
- Alzheimer's disease prediction using deep learning and XAI based interpretable feature selection from blood gene expression data.Scientific reports · 2026Article
- Hybrid deep learning framework for accurate classification of high dimensional genomic data.Scientific reports · 2026Article
- TC check: a web app for thyroid cancer recurrence prediction using explainable machine learning.Journal of cancer research and clinical oncology · 2025Article
- Multimodal feature-optimized approaches for cancer classification using microarray gene expression analysis.Scientific reports · 2025Article
- Predicting cancer risk using machine learning on lifestyle and genetic data.Scientific reports · 2025Article
- A hybrid approach with metaheuristic optimization and random forest in improving heart disease prediction.Scientific reports · 2025Article
- Feature selection via robust weighted score for high dimensional binary class-imbalanced gene expression data.Heliyon · 2024Article
- RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques.Journal of cancer research and clinical oncology · 2024Article
- Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights.Cancer immunology, immunotherapy : CII · 2024Article
- Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making.Scientific reports · 2024Article
- Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.Scientific reports · 2024Article
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- Evaluating classification tools for the prediction of in-vitro microbial pyruvate yield from organic carbon sources.PloS one · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression datasets offer a wide range of information about various biological processes. However, it is difficult to find the important genes among the high-dimensional biological data due to the existence of redundant and unimportant ones. Numerous Feature Selection (FS) techniques have been created to get beyond this obstacle. Improving the efficacy and precision of FS methodologies is crucial in order to identify significant genes amongst complicated complex biological data. In this work, we present a novel approach to gene selection called the Sine Cosine and Cuckoo Search Algorithm (SCACSA). This hybrid method is designed to work with well-known machine learning classifiers Support Vector Machine (SVM). Using a dataset on breast cancer, the hybrid gene selection algorithm's performance is carefully assessed and compared to other feature selection methods. To improve the quality of the feature set, we use minimum Redundancy Maximum Relevance (mRMR) as a filtering strategy in the first step. The hybrid SCACSA method is then used to enhance and optimize the gene selection procedure. Lastly, we classify the dataset according to the chosen genes by using the SVM classifier. Given the pivotal role gene selection plays in unraveling complex biological datasets, SCACSA stands out as an invaluable tool for the classification of cancer datasets. The findings help medical practitioners make well-informed decisions about cancer diagnosis and provide them with a valuable tool for navigating the complex world of gene expression data.
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