ArticleJournal of cancer research and clinical oncology2024
RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques.
Article in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Robust feature selection for cancer microarray data using a hybrid mRMR and Binary Lion Optimization Algorithm.Scientific reports · 2026Article
- Hybrid tuned deep learning model for breast cancer diagnosis using genetic data.Scientific reports · 2026Article
- Hybrid deep learning framework for accurate classification of high dimensional genomic data.Scientific reports · 2026Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Discriminative biomarker selection using hybrid multi-population evolutionary computation.Scientific reports · 2025Article
- Multimodal feature-optimized approaches for cancer classification using microarray gene expression analysis.Scientific reports · 2025Article
- TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis.Scientific reports · 2025Article
- Predicting cancer risk using machine learning on lifestyle and genetic data.Scientific reports · 2025Article
- Variation in bulk RNA-seq and estimated cell type proportion using deconvolution when comparing pancreatic cancer samples within the same individual.medRxiv : the preprint server for health sciences · 2025Article
- SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study.Scientific reports · 2025Article
- Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.Journal of medical systems · 2025Article
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.BMC medical informatics and decision making · 2025Article
- Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.BMC medical informatics and decision making · 2025Article
- Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification.Scientific reports · 2025Article
- Bulk RNA-seq deconvolution heterogeneity across paired pancreatic cancer human samples.Frontiers in genetics · 2025Article
- Transforming Cancer Classification: The Role of Advanced Gene Selection.Diagnostics (Basel, Switzerland) · 2024Article
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4 authors.
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Abstract
problemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.
aimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data's high dimensionality and complexity.
methodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model's performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.
resultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.
conclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.
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