使用多种机器学习工具的表达数据诊断三阴性乳腺癌。
Diagnosis of triple negative breast cancer using expression data with several machine learning tools.
作者信息
Pranaya Sankaranarayanan, Ragunath P K, Venkatesan P
机构信息
Department of Bioinformatics, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai - 600 116, India.
Department of Statistics, ICMR, National Institute for Research in Tuberculosis, Chetpet, Chennai - 600 031, India.
出版信息
Bioinformation. 2022 Apr 30;18(4):325-330. doi: 10.6026/97320630018325. eCollection 2022.
Breast cancer is one of the top three commonly caused cancers worldwide. Triple Negative Breast Cancer (TNBC), a subtype of breast cancer, lacks expression of the oestrogen receptor, progesterone receptor, and HER2. This makes the prognosis poor and early detection hard. Therefore, AI based neural models such as Binary Logistic Regression, Multi-Layer Perceptron and Radial Basis Functions were used for differential diagnosis of normal samples and TNBC samples collected from signal intensity data of microarray experiment. Genes that were significantly upregulated in TNBC were compared with healthy controls. The MLP model classified TNBC and normal cells with anaccuracy of 93.4%. However, RBF gave 74% accuracy and binary Logistic Regression model showed an accuracy of 90.0% in identifying TNBC cases.
乳腺癌是全球最常见的三大癌症之一。三阴性乳腺癌（TNBC）是乳腺癌的一种亚型，缺乏雌激素受体、孕激素受体和HER2的表达。这使得其预后较差且早期检测困难。因此，基于人工智能的神经模型，如二元逻辑回归、多层感知器和径向基函数，被用于从微阵列实验的信号强度数据中收集的正常样本和TNBC样本的鉴别诊断。将TNBC中显著上调的基因与健康对照进行比较。多层感知器模型对TNBC和正常细胞进行分类的准确率为93.4%。然而，径向基函数模型的准确率为74%，二元逻辑回归模型在识别TNBC病例时的准确率为90.0%。
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