使用图神经网络从高维不对称药物数据集中预测药物效应：多靶点药物效应预测的综合分析
Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction
AI总结：
针对高维不对称数据集下图神经网络预测药物效应性能不佳的问题，提出新的过采样技术以改善数据不平衡，并开发混合GNN模型在多标签分类任务上显著优于其他机器学习模型。
摘要：
图神经网络（GNNs）已成为从药物分子图中预测药物效应的最有效的机器学习（ML）技术之一。尽管具有巨大潜力，但当使用包含高维、不对称共现药物效应作为靶点且靶点间存在复杂相关性的数据集时，GNN模型的性能会下降。为每种药物效应训练单独的学习模型，并为广泛的药物效应整合每一个预测结果是不切实际的。因此，存在一个机会，即将此挑战作为多靶点预测问题来解决，并一次性预测所有药物效应。我们开发了标准和混合GNN来执行两个独立的任务：针对连续值的多回归和针对数据集中包含的分类值的多标签分类。由于多标签分类使目标数据更加稀疏，并引入了不对称的标签共现，学习这些模型变得困难，并严重影响了GNN的性能。为了应对这些挑战，我们提出了一种新的数据过采样技术，以在所有给定的不平衡分子图数据集上提高多标签分类性能。使用该技术，我们在保护数据集完整性的同时，改善了药物效应的数据不平衡比率。最后，我们在通过所提出的过采样技术获得的所有过采样数据集上，评估了性能最佳的混合GNN模型的多标签分类性能。在所有评估指标（即精确率、召回率和F1分数）上，该模型显著优于其他ML模型，包括在原始数据集或使用MLSMOTE（一种著名的过采样技术）过采样的数据集上训练的GNN模型。
英文摘要：
Graph neural networks (GNNs) have emerged as one of the most effective ML techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using datasets that contain high-dimensional, asymmetrically co-occurrent drug effects as targets with complex correlations between them. Training individual learning models for each drug effect and incorporating every prediction result for a wide spectrum of drug effects are impractical. Therefore, an opportunity exists to address this challenge as multitarget prediction problems and predict all drug effects at a time. We developed standard and hybrid GNNs to perform two separate tasks: multiregression for continuous values and multilabel classification for categorical values contained in our datasets. Because multilabel classification makes the target data even more sparse and introduces asymmetric label co-occurrence, learning these models becomes difficult and heavily impacts the GNN's performance. To address these challenges, we propose a new data oversampling technique to improve multilabel classification performances on all the given imbalanced molecular graph datasets. Using the technique, we improve the data imbalance ratio of the drug effects while protecting the datasets' integrity. Finally, we evaluate the multilabel classification performance of the best-performing hybrid GNN model on all the oversampled datasets obtained from the proposed oversampling technique. In all the evaluation metrics (i.e., precision, recall, and F1 score), this model significantly outperforms other ML models, including GNN models when they are trained on the original datasets or oversampled datasets with MLSMOTE, which is a well-known oversampling technique.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
