Cancer is a one of the severest diseases and cancer classification plays an important role in cancer diagnosis and treatment. Some different cancers even have similar molecular features such as DNA copy number variant. Pan-cancer classification is still non-trivial at molecular level. Herein, we propose a computational method to classify cancer types by using the self-normalizing neural network (SNN) for analyzing pan-cancer copy number variation data. Since the dimension of the copy number variation features is high, the Monte Carlo feature selection method was used to rank these features. Then a classifier was built by SNN and feature selection method to select features. Three thousand six hundred ninety-four features were chosen for the prediction model, which yields the accuracy value is 0.798 and macro F1 is 0.789. We compared our model to random forest method. Results show the accuracy and macro F1 obtained by our classifier are higher than those obtained by random forest classifier, indicating the good predictive power of our method in distinguishing four different cancer types. This method is also extendable to pan-cancer classification for other molecular features.

中文翻译：

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基于自归一化神经网络和特征选择的泛癌分类

癌症是最严重的疾病之一，癌症分类在癌症诊断和治疗中起着重要作用。一些不同的癌症甚至具有相似的分子特征，例如 DNA 拷贝数变异。泛癌分类在分子水平上仍然很重要。在此，我们提出了一种通过使用自归一化神经网络 (SNN) 来分析泛癌拷贝数变异数据来对癌症类型进行分类的计算方法。由于拷贝数变异特征的维数较高，因此采用蒙特卡罗特征选择方法对这些特征进行排序。然后通过SNN和特征选择方法构建分类器来选择特征。为预测模型选择了 3694 个特征，其精度值为 0.798，宏 F1 为 0.789。我们将我们的模型与随机森林方法进行了比较。结果表明，我们的分类器获得的准确率和宏 F1 高于随机森林分类器的结果，表明我们的方法在区分四种不同的癌症类型方面具有良好的预测能力。该方法还可扩展到其他分子特征的泛癌分类。