Ke Shanbao, Huang Yuxuan, Wang Dong, Jiang Qiang, Luo Zhangyang, Li Baiyu, Yan Danfang, Zhou Jianwei
Department of Oncology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, China.
Department of Neuroscience in the Behavioral Sciences, Duke University and Duke Kunshan University, Suzhou, China.
Front Med (Lausanne). 2024 Nov 6;11:1482726. doi: 10.3389/fmed.2024.1482726. eCollection 2024.
Breast cancer is a prevalent malignancy and one of the leading causes of cancer-related mortality among women worldwide. This disease typically manifests through the abnormal proliferation and dissemination of malignant cells within breast tissue. Current diagnostic and therapeutic strategies face significant challenges in accurately identifying and localizing specific subtypes of breast cancer. In this study, we developed a novel machine learning-based predictor, BreCML, designed to accurately classify subpopulations of breast cancer cells and their associated marker genes. BreCML exhibits outstanding predictive performance, achieving an accuracy of 98.92% on the training dataset. Utilizing the XGBoost algorithm, BreCML demonstrates superior accuracy (98.67%), precision (99.15%), recall (99.49%), and F1-score (99.79%) on the test dataset. Through the application of machine learning and feature selection techniques, BreCML successfully identified new key genes. This predictor not only serves as a powerful tool for assessing breast cancer cellular status but also offers a rapid and efficient means to uncover potential biomarkers, providing critical insights for precision medicine and therapeutic strategies.
乳腺癌是一种常见的恶性肿瘤，也是全球女性癌症相关死亡的主要原因之一。这种疾病通常通过乳腺组织内恶性细胞的异常增殖和扩散表现出来。目前的诊断和治疗策略在准确识别和定位乳腺癌的特定亚型方面面临重大挑战。在本研究中，我们开发了一种基于机器学习的新型预测器BreCML，旨在准确分类乳腺癌细胞亚群及其相关标记基因。BreCML表现出出色的预测性能，在训练数据集上的准确率达到98.92%。利用XGBoost算法，BreCML在测试数据集上展示了卓越的准确率（98.67%）、精确率（99.15%）、召回率（99.49%）和F1分数（99.79%）。通过应用机器学习和特征选择技术，BreCML成功识别出了新的关键基因。这个预测器不仅是评估乳腺癌细胞状态的有力工具，还提供了一种快速有效的方法来发现潜在的生物标志物，为精准医学和治疗策略提供了关键见解。
