Nolte Daniel, Bazgir Omid, Pal Ranadip
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.
Modeling & Simulation/Clinical Pharmacology, Genentech, South San Francisco, CA, USA.
Methods Mol Biol. 2025;2932:259-271. doi: 10.1007/978-1-0716-4566-6_14.
Over the past decade, convolutional neural networks (CNNs) have revolutionized predictive modeling of data containing spatial correlations, specifically excelling at image analysis tasks due to their embedded feature extraction and improved generalization. However, outside of image or sequence data, datasets typically lack the structural correlation needed to exploit the benefits of CNN modeling. This is especially true regarding anticancer drug sensitivity prediction tasks, as the data used is often tabular without any embedded information in the ordering or locations of the features when utilizing data other than DNA or RNA sequences. This chapter provides a computational procedure, REpresentation of Features as Images with NEighborhood Dependencies (REFINED), that maps high-dimensional feature vectors into compact 2D images suitable for CNN-based deep learning. The pairing of REFINED mappings with CNNs enables enhanced predictive performance through reduced model parameterization and improved embedded feature extraction as compared to fully connected alternatives utilizing the high-dimensional feature vectors.
在过去十年中，卷积神经网络（CNN）彻底改变了对包含空间相关性的数据进行预测建模的方式，特别是由于其嵌入式特征提取和改进的泛化能力，在图像分析任务中表现出色。然而，在图像或序列数据之外，数据集通常缺乏利用CNN建模优势所需的结构相关性。在抗癌药物敏感性预测任务中尤其如此，因为在使用除DNA或RNA序列之外的数据时，所使用的数据通常是表格形式，特征的排序或位置中没有任何嵌入式信息。本章提供了一种计算程序，即具有邻域依赖性的特征图像表示（REFINED），它将高维特征向量映射到适合基于CNN的深度学习的紧凑二维图像中。与使用高维特征向量的全连接替代方案相比，REFINED映射与CNN的配对通过减少模型参数化和改进嵌入式特征提取，实现了更高的预测性能。
