Background: Predicting networks of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an end-to-end manner. Results: We give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an inter-compound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the inter-compound network in an end-to-end manner. Conclusions: Experiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremely-sparse networks.

No abstract

Outcome estimation of treatments for individual targets is a crucial foundation for decision making based on causal relations. Most of the existing outcome estimation methods deal with binary or multiple-choice treatments; however, in some applications, the number of interventions can be very large, while the treatments themselves have rich information. In this study, we consider one important instance of such cases, that is, the outcome estimation problem of graph-structured treatments such as drugs. Due to the large number of possible interventions, the counterfactual nature of observational data, which appears in conventional treatment effect estimation, becomes a more serious issue in this problem. Our proposed method GraphITE (pronounced 'graphite') obtains the representations of the graph-structured treatments using graph neural networks, and also mitigates the observation biases by using the HSIC regularization that increases the independence of the representations of the targets and the treatments. In contrast with the existing methods, which cannot deal with "zero-shot" treatments that are not included in observational data, GraphITE can efficiently handle them thanks to its capability of incorporating graph-structured treatments. The experiments using the two real-world datasets show GraphITE outperforms baselines especially in cases with a large number of treatments.

Treatment effect estimation is a fundamental problem in various domains for effective decision making. While many studies assume that observational data include all the confounding variables, we cannot practically guarantee that observational data include such confounding variables, and there might be confounding variables that are not included in observational data, referred to as hidden confounding variables. Recently, variational autencoder (VAE) based methods have been successfully applied to treatment effect estimation problem. However, although they can recover a large class of latent variable models, they do not give the correct treatment effect, even when they achieve an optimal solution due to the nature of VAE loss function. We propose an efficient VAE-based method that employs information theory to estimate treatment effect and combines it with a matching technique. To the best of our knowledge, this is the first work that gives the correct treatment effect given an optimal solution using VAE-based methods. Experiments on a semi-real dataset and synthetic dataset demonstrate that the proposed method mitigates VAE problems and observational bias effectively, even under hidden confounding variables, and outperforms strong baseline methods.

Individual treatment effect (ITE) represents the expected improvement in the outcome of taking a particular action to a particular target, and plays important roles in decision making in various domains. However, its estimation problem is difficult because intervention studies to collect information regarding the applied treatments (i.e., actions) and their outcomes are often quite expensive in terms of time and monetary costs. In this study, we consider a semi-supervised ITE estimation problem that exploits more easily-available unlabeled instances to improve the performance of ITE estimation using small labeled data. We combine two ideas from causal inference and semi-supervised learning, namely, matching and label propagation, respectively, to propose Counterfactual Propagation; CP which is the first semi-supervised ITE estimation method. Experiments using semi-real datasets demonstrate that the proposed method can successfully mitigate the data scarcity problem in ITE estimation.