Abstract：
Copy number variation (CNV) is an essential genetic driving factor of cancer formation and progression, making intelligent classification based on CNV feasible. However, there are a few challenges in the current machine learning and deep learning methods, such as the design of base classifier combination schemes in ensemble methods and the selection of layers of neural networks, which often result in low accuracy. Therefore, an adaptive bilinear dynamic cascade model (Adap-BDCM) is developed to further enhance the accuracy and applicability of these methods for intelligent classification on CNV datasets. In this model, a feature selection module is introduced to mitigate the interference of redundant information, and a bilinear model based on the gated attention mechanism is proposed to extract more beneficial deep fusion features. Furthermore, an adaptive base classifier selection scheme is designed to overcome the difficulty of manually designing base classifier combinations and enhance the applicability of the model. Lastly, a novel feature fusion scheme with an attribute recall submodule is constructed, effectively avoiding getting stuck in local solutions and missing some valuable information. Numerous experiments have demonstrated that our Adap-BDCM model exhibits optimal performance in cancer classification, stage prediction, and recurrence on CNV datasets. This study can assist physicians in making diagnoses faster and better.
摘要：
拷贝数变异（CNV）是癌症形成和进展的重要遗传驱动因素，使基于CNV的智能分类变得可行。然而,当前的机器学习和深度学习方法存在一些挑战，例如集成方法中的基分类器组合方案的设计和神经网络层的选择，这往往导致低精度。因此,开发了一种自适应双线性动态级联模型（Adap-BDCM），以进一步提高这些方法对CNV数据集进行智能分类的准确性和适用性。在这个模型中,引入了特征选择模块，以减轻冗余信息的干扰，提出了一种基于门控注意机制的双线性模型来提取更多有益的深度融合特征。此外，设计了一种自适应的基分类器选择方案，克服了人工设计基分类器组合的困难，增强了模型的适用性。最后,构造了一种具有属性召回子模块的新颖特征融合方案，有效避免陷入本地解决方案和丢失一些有价值的信息。大量实验表明，我们的Adap-BDCM模型在癌症分类中表现出最佳性能，阶段预测，和CNV数据集的复发。这项研究可以帮助医生更快更好地进行诊断。
