A multi-task graph convolutional network modeling of drug-drug interactions and synergistic efficacy

Abstract Although the polypharmacy has both higher therapeutic efficacy and less drug resistance in combating complex diseases, drug-drug interactions (DDIs) may trigger unexpected pharmacological effects, such as side effects, adverse reactions, or even serious toxicity. Thus, it is crucial to identify DDIs and explore its underlying mechanism (e.g., DDIs types) for polypharmacy safety. However, the detection of DDIs in assays is still time-consuming and costly, due to the need of experimental search over a large drug combinational space. Machine learning methods have been proved as a promising and efficient method for preliminary DDI screening. Most shallow learning-based predictive methods focus on whether a drug interacts with another or not. Although deep learning (DL)-based predictive methods address a more realistic screening task for identifying the DDI types, they only predict the DDI types of known DDI, ignoring the structural relationship between DDI entries, and they also cannot reveal the knowledge about the dependence between DDI types. Thus, here we proposed a novel end-to-end deep learning-based predictive method (called MTDDI) to predict DDIs as well as its types, exploring the underlying mechanism of DDIs. MTDDI designs an encoder derived from enhanced deep relational graph convolutional networks to capture the structural relationship between multi-type DDI entries, and adopts the tensor-like decoder to uniformly model both single-fold interactions and multi-fold interactions to reflect the relation between DDI types. The results show that our MTDDI is superior to other state-of-the-art deep learning-based methods. For predicting the multi-type DDIs with unknown DDIs in case of both single-fold DDIs and multi-fold DDIs, we validated the effectiveness and the practical capability of our MTDDI. More importantly, MTDDI can reveal the dependency between DDI types. These crucial observations are beneficial to uncover the mechanism and regularity of DDIs.

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Path Aggregation Model for Attributed Network Embedding with a Smart Random Walk

Journal of Physics Conference Series ◽

10.1088/1742-6596/2082/1/012011 ◽

2021 ◽

Vol 2082 (1) ◽

pp. 012011

Author(s):

Xiang Xiao ◽

Kang Zhang ◽

Shuang Qiu ◽

Wei Liu

Keyword(s):

Random Walk ◽

High Order ◽

Edge Weight ◽

Task Graph ◽

Network Embedding ◽

Convolutional Network ◽

Aggregation Model ◽

Sample Paths ◽

Attributed Network ◽

Walk Algorithm

Abstract Network embedding has attracted a surge of attention recently. In this field, how to preserve high-order proximity has long been a difficult task. Graph convolutional network (GCN) and random walk-based approaches can preserve high-order proximity to a certain extent. However, they partially concentrate on the aggregation process and sampling process respectively. Path aggregation methods combine the merits of GCN and random walk, and thus can preserve more high-order information and achieve better performance. However, path aggregation framework has not been applied in attributed network embedding yet. In this paper, we propose a path aggregation model for attributed network embedding, with two main contributions. First, we claim that there always exists implicit edge weight in networks, and design a tweaked random walk algorithm to sample paths accordingly. Second, we propose a path aggregation framework dealing with both nodes and attributes. Extensive experimental results show that our proposal outperforms the cutting-edge baselines on downstream tasks, such as node clustering, node classification, and link prediction.

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