A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This ‘$n≪p$’ property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating known functional relationships between the biological units, such as the graph-embedded deep feedforward network (GEDFN) model, has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection.

中文翻译：

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forgeNet：使用基于树的集成分类器进行特征图构建的图深度神经网络模型。

在组学数据的预测模型构建中，一个独特的挑战是样本数量（n）与大量特征（p）的相对比。这个 '$ñ≪p$属性为使用深度学习技术进行疾病结局分类带来了困难。通过合并生物单元之间已知的功能关系（例如，图嵌入的深度前馈网络（GEDFN）模型）进行稀疏学习已成为解决此问题的方法。但是，这样的方法需要现有的特征图，并且特征图的潜在错误指定可能对分类和特征选择有害。