The quantification of similarities among human diseases is crucial for enhancing our understanding of disease biology, which can aid in improving disease diagnosis, prognosis and treatment, and drug development. Recently, efforts have been devoted to quantifying disease similarity by integrating multi-view data sources from disparate biological data. However, disease data are often sparse due to their rarity or privacy, leading to suboptimal representation of disease when biological entity relationships and labeled disease data are not adequately modeled. To address this challenge, we propose DAISY (Dual-grAnularity contrastIve learning for diSease similaritY prediction), an effective deep learning model for disease representation modeling. DAISY leverages a novel dual-granularity contrastive learning mechanism to enhance the representation of diverse biological entities. Its ability to enable the collaborative supervision of diseases represented by both homogeneous and heterogeneous information is of great significance, leading to a high level of disease representation learning. Besides, this contrastive learning mechanism combines two independent and complementary components: a hierarchical biological entity relationship-aware module to capture disease features on other biological entities, and a disease association capturing module based on signed random walk with precious disease data. Experimental results demonstrate that DAISY achieves outstanding performance on the disease similarity prediction problem.

Polypharmacy side effect prediction is a vital task in healthcare machine learning, aiming to predict the occurrence of multiple side effects when patients take drugs together. Existing researches mainly follow the deep encoder-decoder paradigm and suffer from the following two limitations: 1) The encoder simply combines inadequate information about drugs; 2) The decoder fails to capture dependencies among different side effects, thus hindering accurate prediction. To overcome the aforementioned limitations, we propose a boundary-guided polypharmacy side effect prediction method (BACON). Our framework constructs two complementary views based on drug’s chemical substructure and biochemical features and enhances drug representations via contrastive learning. The decoder incorporates a boundary-guided strategy to capture drug interaction dependencies for optimizing polypharmacy side effect prediction. Experimental results demonstrate BACON superiority over SOTA models in accurately predicting drug side effect events.

Quantifying similarities among human diseases is crucial to enhance our understanding of disease biology. Deep learning efforts have been devoted to quantifying disease similarity by integrating multi-view data sources from disparate biological data. However, disease data are often sparse, leading to suboptimal representation of disease given biological entity relationships and labeled disease data are not adequately modeled. In this paper, we propose an effective Synergistic disease Similarity measurement model called SynerSim. SynerSim possesses two key components: a hierarchical biological entity relation perception module to capture disease features from various biological entities, and a disease association capturing module based on signed random walk to model precious disease data. Additionally, SynerSim leverages dual granularity contrastive learning to enhance the representation of diverse biological entities, owing to the ability to enable the synergistic supervision of diseases represented by both homogeneous and heterogeneous information. Experimental results demonstrate that SynerSim achieves outstanding performance in the disease similarity measurement.

MOTIVATION: An imperative step in drug discovery is the prediction of drug-disease associations (DDAs), which tries to uncover potential therapeutic possibilities for already validated drugs. It is costly and time-consuming to predict DDAs using wet experiments. Graph Neural Networks as an emerging technique have shown superior capacity of dealing with DDA prediction. However, existing Graph Neural Networks-based DDA prediction methods suffer from sparse supervised signals. As graph contrastive learning has shined in mitigating sparse supervised signals, we seek to leverage graph contrastive learning to enhance the prediction of DDAs. Unfortunately, most conventional graph contrastive learning-based models corrupt the raw data graph to augment data, which are unsuitable for DDA prediction. Meanwhile, these methods could not model the interactions between nodes effectively, thereby reducing the accuracy of association predictions. RESULTS: A model is proposed to tap potential drug candidates for diseases, which is called Similarity Measures-based Graph Co-contrastive Learning (SMGCL). For learning embeddings from complicated network topologies, SMGCL includes three essential processes: (i) constructs three views based on similarities between drugs and diseases and DDA information; (ii) two graph encoders are performed over the three views, so as to model both local and global topologies simultaneously; and (iii) a graph co-contrastive learning method is introduced, which co-trains the representations of nodes to maximize the agreement between them, thus generating high-quality prediction results. Contrastive learning serves as an auxiliary task for improving DDA predictions. Evaluated by cross-validations, SMGCL achieves pleasing comprehensive performances. Further proof of the SMGCL's practicality is provided by case study of Alzheimer's disease. AVAILABILITY AND IMPLEMENTATION: https://github.com/Jcmorz/SMGCL.

Numerous clinical trials have revealed that a serious consequence of polypharmacy is that patients are at high risk of adverse side effects. However, designing clinical trials to determine the frequency of side effects from polypharmacy is both time-consuming and costly. Therefore, the computer-aided prediction of drug side effects is becoming an attractive proposition. Existing methods of drug side effects prediction introduce the target protein of a drug without screening. Although this alleviates the sparsity of the original data to some extent, the blind introduction of proteins as auxiliary information allows a large amount of noisy information to be added, which degrades the model efficiency and acheive sub-opitmal predicition results. To this end, we propose a novel method called DEP-GCN (Drug Side Effects Prediction via Heterogeneous Multi-Relational Graph Convolutional Networks). Specifically, we design two protein auxiliary pathways directly related to drugs and combine these two auxiliary pathways with a multi-relational graph of drug side effects, which both alleviate the sparsity of data and filter out noisy data. Then, to produce accurate drug representations, we distinguish the impact from different drug neighbors and introduce a query-aware attention mechanism to fine-grained determine how much messaging is delivered. Finally, in contrast to approaches limited to predicting the existence or associations of drug side effects, we output the exact frequency of drug side effects occurring via a tensor factorization decoder. Extensive experimental results demonstrate that DEP-GCN significantly outperforms all baseline methods. The further examination provides literature evidence for highly ranked predictions.

Session-based recommendation (SBR) aims to predict the next item based on anonymous behavior session, which has become increasingly essential in various online services. Prior efforts mainly focus on modeling user preference based on the current session. Although some of them have been proven effective, they fail to address two main challenges in SBR. First, SBR suffers more from the problem of data sparsity due to the very limited user–item interactions, and hence it cannot sufficiently capture complicated item dependency relationships. Second, most of the user-item interaction sequences may be with noisy preference signals due to the uncertainty of user's behaviors, and it is difficult to distill high-quality item for recommendation. In this study, we propose a novel SBR model that exploits Cross-session information for Knowledge-aware Session-based Recommendation (CKSR) to address these two issues. Specifically, cross-session graph and knowledge graph are combined to model a cross-session knowledge graph, based on which a knowledge-aware attention mechanism is performed to capture the complicated transition pattern among interacted items. Each session is then represented as the composition of the global preference and the current interest of that session. Moreover, we leverage the similar sessions for the target session to establish a similar session referral circle and apply an influence coupler to judge the significance of different session referrals. An attentive network is designed to distill session preferences from its unique session referral circle. It dynamically extracts high-quality item from noisy session. Experiments on two benchmark data sets demonstrate that CKSR outperforms the state-of-the-art methods consistently.