School of Advanced Science and Language, VIT Bhopal University, Kothrikalan, Sehore, Bhopal, 466114, India.
Planning Department, State Planning Institute (New Division), Lucknow, Utter Pradesh, 226001, India.
J Cancer Res Clin Oncol. 2024 Oct 10;150(10):455. doi: 10.1007/s00432-024-05968-z.
Breast 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.
This 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.
We 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.
The 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.
Our 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.
乳腺癌是一个全球性的主要健康问题，导致女性死亡率居高不下。由于基因表达数据的高维性和复杂性，早期检测的挑战更加严峻，这使得分类过程变得复杂。
本研究旨在开发一种先进的深度学习模型，使用 RNA-Seq 基因表达数据准确检测乳腺癌，同时有效解决数据高维性和复杂性带来的挑战。
我们提出了一种新颖的混合基因选择方法，将哈里斯鹰优化（HHO）和鲸鱼优化（WO）算法与深度学习相结合，以提高特征选择和分类准确性。将该模型的性能与五种与深度学习集成的传统优化算法进行了比较：遗传算法（GA）、人工蜂群算法（ABC）、布谷鸟搜索算法（CS）和粒子群优化算法（PSO）。RNA-Seq 数据来自印度博帕尔的贾瓦哈拉尔·尼赫鲁癌症医院和研究中心的 66 对正常和癌症组织的乳腺癌患者。测序由印度班加罗尔的 Biokart Genomics 实验室完成。
所提出的模型实现了 99.0%的平均分类准确率，始终优于 GA、ABC、CS 和 PSO 方法。该数据集包含 55 名女性乳腺癌患者，包括早期和晚期患者以及年龄匹配的健康对照组。
我们的研究结果表明，使用 HHO 和 WO 的混合基因选择方法结合深度学习是一种强大而准确的乳腺癌检测工具。这种方法具有早期检测的潜力，并可以促进个性化治疗策略，最终改善患者的预后。
