论文标题
对抗癌药物敏感性预测的精制CNN集合学习的研究
Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction
论文作者
论文摘要
使用深度学习模型进行单个细胞系的抗癌药物敏感性预测是个性化医学的重大挑战。基于CNN（卷积神经网络）的精制（表示具有邻居依赖性图像的特征）模型已显示出有希望的药物敏感性预测结果。精致CNN背后的主要思想代表高维矢量，作为具有空间相关性的紧凑图像，可以从卷积神经网络体系结构中受益。但是，由于经过考虑的距离测量和邻域的变化，从向量到紧凑型2D图像的映射不是唯一的。在本文中，我们考虑了基于从此类映射构建的合奏的预测，这些映射可以改善最佳的单个精制CNN模型预测。使用NCI60和NCIALMANAC数据库所示的结果表明，与单个模型相比，整体方法可以提供显着的性能改善。我们进一步说明，由不同映射的合并创建的单个映射可以提供类似于堆叠集合但计算复杂性明显较低的性能。
Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network) based models have shown promising results in drug sensitivity prediction. The primary idea behind REFINED CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from convolutional neural network architectures. However, the mapping from a vector to a compact 2D image is not unique due to variations in considered distance measures and neighborhoods. In this article, we consider predictions based on ensembles built from such mappings that can improve upon the best single REFINED CNN model prediction. Results illustrated using NCI60 and NCIALMANAC databases shows that the ensemble approaches can provide significant performance improvement as compared to individual models. We further illustrate that a single mapping created from the amalgamation of the different mappings can provide performance similar to stacking ensemble but with significantly lower computational complexity.
