PDF(Pubmed)
Abstract：
Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-to-end learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model\'s superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer.
摘要：
简介:协同用药,癌症治疗的关键治疗策略，涉及结合多种药物以增强治疗效果和减轻副作用。当前的研究主要采用深度学习模型从细胞系和癌症药物结构数据中提取特征。然而,这些方法往往忽略了数据中复杂的非线性关系，忽略基因表达数据在多维空间中的分布特征和加权概率密度。它也未能充分利用癌症药物的结构信息和药物分子之间的潜在相互作用。方法：为了克服这些挑战，我们引入了专门为癌症药物量身定制的创新端到端学习模式，图协同表示网络(DKPEGraphSYN)的双核密度和位置编码(DKPE)。该模型被设计用于改进癌症中药物组合协同作用的预测。DKPE-GraphSYN利用双核密度估计和位置编码技术，有效捕获基因表达的加权概率密度和空间分布信息，同时通过图神经网络探索癌症药物分子之间的相互作用和潜在关系。结果：实验结果表明，我们的预测模型在全面的癌症药物和细胞系协同数据集上预测药物协同作用方面取得了显着的性能增强，实现0.969的AUPR和0.976的AUC。讨论：这些结果证实了我们的模型在预测癌症药物组合方面的卓越准确性，为癌症的临床用药策略提供了一种支持方法。
