Drug repositioning (DR), identifying new uses for approved drugs, accelerates drug discovery. To address label sparsity in inferring drug-disease associations (DDAs), we propose DCDR, a heterogeneous graph contrastive learning method with diffusion and causal representation. DCDR resolves two key issues in computational DR: 1) Semantic degradation in contrastive views: Standard random perturbations damage pharmacological relationships. Our diffusion paradigm generates valid variations via structured noise and fidelity-driven reconstruction, preserving interactions while boosting diversity. 2) Confounding bias in representations: Protein-mediated spurious correlations distort embeddings. Our causal framework eliminates this by separating direct therapeutic effects from confounding paths through protein intervention, counterfactual reasoning, and adaptive fusion, isolating deconfounded semantics.$\mathbf{1 0}$-fold cross-validation on three benchmarks shows DCDR outperforms state-of-the-art methods significantly. A case study confirms its ability to identify biologically plausible candidates for Alzheimer's disease.

Drug repositioning offers a promising strategy to identify novel therapeutic applications for existing drugs. Despite the frequent use of graph neural network in this domain, their efficacy is often hampered by the sparsity of known drug interaction networks. Furthermore, diseases within the same subclass frequently share structural and clinical traits, leading to similar complications and responses to specific treatments among related viruses.To tackle the challenges of sparse drug interaction networks and leverage the shared treatment potential among similar diseases, we propose Dron, a dual-branched contrast-enhanced method for drug repositioning. Dron enhances drug and disease representations using a branched contrastive loss strategy, and improves identification accuracy by aggregating features from related categories. Validation across multiple public datasets shows that Dron significantly enhances drug repositioning performance. Molecular docking experiments on Alzheimer’s disease further confirm Dron’s effectiveness in addressing complex diseases.

In the realm of medicine and healthcare, adverse drug reactions (ADRs) are a significant contributor to mortality and morbidity. Consequently, it is of paramount importance to closely observe the adverse effects of marketed drugs to minimize associated risks. While current methods for Adverse Drug Reaction (ADR) detection have demonstrated notable efficacy, a significant number of researchers have failed to acknowledge the integral role that sentiment information plays in this process. In this paper, we propose Sentiment Enhanced Dual Graph Convolutional Networks (SEDGCN), a novel method for ADRs detection by incorporating sentiment information. In particular, we first introduce the concept of prompt learning and reformulate the ADR detection task as an aspect-level sentiment analysis task. Subsequently, we construct a sentimentenhanced dependency matrix for each sentence to capture the sentiment knowledge and syntactic information of the sentence. The matrix is then input into the graph convolutional networks to obtain a graph representation of the sentence. Finally, to capture global information, we construct a heterogeneous graph based on all words and sentences and fuse this heterogeneous graph with the sentence-level graph representation for ADR detection. Extensive experimentation on two publicly available datasets, namely TwiMed and Twitter, yielded F1 scores of 78.24% and 75.43%, respectively. These results underscore the efficacy of our proposed model.

Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices.

The emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have created an enormous socioeconomic impact. Although there are several promising drug candidates in clinical trials, none of them are approved yet. Thus, the drug repositioning approach may help to overcome the current pandemic. However, the sparse dataset of COVID-19 limits the accuracy of existing drug repositioning. To overcome this problem, we propose a novel drug repositioning framework (named Drug2Cov). Drug2Cov can learn an effective representation via integrating self-supervised learning with sparse data. Meanwhile, Drug2Cov uses a heterogeneous graph neural network to capture the complex interaction between viruses, targets, and drugs that enhance the accuracy of drug repositioning. The experimental results demonstrate the effectiveness and feasibility of our proposed Drug2Cov framework. Source code and dataset are freely available at https://github.com/lhf3291109/Drug2Cov.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of coronavirus that causes coronavirus disease 2019 (COVID-19), which leads to over 800,000 deaths and is still no specific medicines. Drug repositioning aiming to infer potential drugs for diseases and achieve much attention during the SARS-CoV-2 epidemic. However, find a specific drug of SARS-CoV-2 is still a large challenge that cannot be addressed well with current methods. To overcome this problem, we present a novel drug repositioning framework of heterogeneous graph convolutional networks for SARS-CoV2. The deep2CoV model can effectively search the potential drugs for SARS-CoV-2, which reduce the number of clinical trials and drug development cycles. The experimental results demonstrate the effectiveness and feasibility of our proposed deep2CoV framework.