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MOTIVATION: Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. RESULTS: We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.

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Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into HGNNs to associate nodes closely related in semantics but far apart in the graph. However, existing metapath-based models suffer from either information loss or high computation costs. To address these problems, we present a novel Metapath Context Convolution-based Heterogeneous Graph Neural Network (MECCH). MECCH leverages metapath contexts, a new kind of graph structure that facilitates lossless node information aggregation while avoiding any redundancy. Specifically, MECCH applies three novel components after feature preprocessing to extract comprehensive information from the input graph efficiently: (1) metapath context construction, (2) metapath context encoder, and (3) convolutional metapath fusion. Experiments on five real-world heterogeneous graph datasets for node classification and link prediction show that MECCH achieves superior prediction accuracy compared with state-of-the-art baselines with improved computational efficiency. The code is available at https://github.com/cynricfu/MECCH.

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Recommender systems (RS) have been increasingly applied to food and health. However, challenges still remain, including the effective incorporation of heterogeneous information and the discovery of meaningful relationships among entities in the context of food and health recommendations. To address these challenges, we propose a novel framework, the Health-aware Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA), for unsupervised representation learning on heterogeneous graph-structured data. HFRS-DA utilizes an attention technique to reconstruct node features and edges and employs a dual hierarchical attention mechanism for enhanced unsupervised learning of attributed graph representations. HFRS-DA addresses the challenge of effectively leveraging the heterogeneous information in the graph and discovering meaningful semantic relationships between entities. The framework analyses recipe components and their neighbours in the heterogeneous graph and can discover popular and healthy recipes, thereby promoting healthy eating habits. We compare HFRS-DA using the Allrecipes dataset and find that it outperforms all the related methods from the literature. Our study demonstrates that HFRS-DA enhances the unsupervised learning of attributed graph representations, which is important in scenarios where labelled data is scarce or unavailable. HFRS-DA can generate node embeddings for unused data effectively, enabling both inductive and transductive learning.

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Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

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The aim of explainable recommendation is not only to provide recommended items to users, but also to make users aware of why these items are recommended. Traditional recommendation methods infer user preferences for items using user-item rating information. However, the expressive power of latent representations of users and items is relatively limited due to the sparseness of the user-item rating matrix. Heterogeneous information networks (HIN) provide contextual information for improving recommendation performance and interpreting the interactions between users and items. However, due to the heterogeneity and complexity of context information in HIN, it is still a challenge to integrate this contextual information into explainable recommendation systems effectively. In this paper, we propose a novel framework-the dual-attention networks for explainable recommendation (DANER) in HINs. We first used multiple meta-paths to capture high-order semantic relations between users and items in HIN for generating similarity matrices, and then utilized matrix decomposition on similarity matrices to obtain low-dimensional sparse representations of users and items. Secondly, we introduced two-level attention networks, namely a local attention network and a global attention network, to integrate the representations of users and items from different meta-paths for obtaining high-quality representations. Finally, we use a standard multi-layer perceptron to model the interactions between users and items, which predict users' ratings of items. Furthermore, the dual-attention mechanism also contributes to identifying critical meta-paths to generate relevant explanations for users. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of DANER on recommendation performance as compared with the state-of-the-art methods. A case study illustrates the interpretability of DANER.

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Existing recommender systems usually make recommendations by exploiting the binary relationship between users and items, and assume that users only have flat preferences for items. They ignore the users' intentions as an origin and driving force for users' performance. Cognitive science tells us that users' preference comes from an explicit intention. They first have an intention to possess a particular (type of) item(s) and then their preferences emerge when facing multiple available options. Most of the data used in recommender systems are composed of heterogeneous information contained in a complicated network's structure. Learning effective representations from these heterogeneous information networks (HINs) can help capture the user's intention and preferences, therefore, improving recommendation performance. We propose a hierarchical user's intention and preferences modeling for sequential recommendation based on relation-aware HIN embedding (HIP-RHINE). We first construct a multirelational semantic space of heterogeneous information networks to learn node embedding based on specific relations. We then model user's intention and preferences using hierarchical trees. Finally, we leverage the structured decision patterns to learn user's preferences and thereafter make recommendations. To demonstrate the effectiveness of our proposed model, we also report on the conducted experiments on three real data sets. The results demonstrated that our model achieves significant improvements in Recall and Mean Reciprocal Rank metrics compared with other baselines.

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The ongoing COVID-19 pandemic constitutes a critical phase for the transnationalization of public spheres. Against this backdrop, we ask how transnational COVID-19 related online discourse has been throughout the EU over the first year of the pandemic. Which events triggered higher transnational coherence or national structuration of this specific issue public on Twitter? In order to study these questions, we rely on Twitter data obtained from the TBCOV database, i.e., a dataset for multilingual, geolocated COVID-19 related Twitter communication. We selected corpora for the 27 member states of the EU plus the United Kingdom. We defined three research periods representing different phases of the pandemic, namely April (1st wave), August (interim) and December 2020 (2nd wave) resulting in a set of 51,893,966 unique tweets for comparative analysis. In order to measure the level and temporal variation of transnational discursive linkages, we conducted a spatiotemporal network analysis of so-called Heterogeneous Information Networks (HINs). HINs allow for the integration of multiple, heterogeneous network entities (hashtags, retweets, @-mentions, URLs and named entities) to better represent the complex discursive structures reflected in social media communication. Therefrom, we obtained an aggregate measure of transnational linkages on a daily base by relating these linkages back to their geolocated authors. We find that the share of transnational discursive linkages increased over the course of the pandemic, indicating effects of adaptation and learning. However, stringent political measures of crisis management at the domestic level (such as lockdown decisions) caused stronger national structuration of COVID-19 related Twitter discourse.

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Heterogeneous information network (HIN) embedding is an important tool for tasks such as node classification, community detection, and recommendation. It aims to find the representations of nodes that preserve the proximity between entities of different nature. A family of approaches that are widely adopted applies random walk to generate a sequence of heterogeneous contexts, from which, the embedding is learned. However, due to the multipartite graph structure of HIN, hub nodes tend to be over-represented to their context in the sampled sequence, giving rise to imbalanced samples of the network. Here, we propose a new embedding method: CoarSAS2hvec. The self-avoiding short sequence sampling with the HIN coarsening procedure (CoarSAS) is utilized to better collect the rich information in HIN. An optimized loss function is used to improve the performance of the HIN structure embedding. CoarSAS2hvec outperforms nine other methods in node classification and community detection on four real-world data sets. Using entropy as a measure of the amount of information, we confirm that CoarSAS catches richer information of the network compared with that through other methods. Hence, the traditional loss function applied to samples by CoarSAS can also yield improved results. Our work addresses a limitation of the random-walk-based HIN embedding that has not been emphasized before, which can shed light on a range of problems in HIN analyses.

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INTRODUCTION: Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. AREAS COVERED: In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies. EXPERT OPINION: Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.

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Uncovering additional long non-coding RNA (lncRNA)-disease associations has become increasingly important for developing treatments for complex human diseases. Identification of lncRNA biomarkers and lncRNA-disease associations is central to diagnoses and treatment. However, traditional experimental methods are expensive and time-consuming. Enormous amounts of data present in public biological databases are available for computational methods used to predict lncRNA-disease associations. In this study, we propose a novel computational method to predict lncRNA-disease associations. More specifically, a heterogeneous network is first constructed by integrating the associations among microRNA (miRNA), lncRNA, protein, drug, and disease, Second, high-order proximity preserved embedding (HOPE) was used to embed nodes into a network. Finally, the rotation forest classifier was adopted to train the prediction model. In the 5-fold cross-validation experiment, the area under the curve (AUC) of our method achieved 0.8328 ± 0.0236. We compare it with the other four classifiers, in which the proposed method remarkably outperformed other comparison methods. Otherwise, we constructed three case studies for three excess death rate cancers, respectively. The results show that 9 (lung cancer, gastric cancer, and hepatocellular carcinomas) out of the top 15 predicted disease-related lncRNAs were confirmed by our method. In conclusion, our method could predict the unknown lncRNA-disease associations effectively.

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Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to capture the semantic subtlety, we propose the concept of aspects with each aspect being a unit representing one underlying semantic facet. Meanwhile, network embedding has emerged as a powerful method for learning network representation, where the learned embedding can be used as features in various downstream applications. Therefore, we are motivated to propose a novel embedding learning framework-ASPEM-to preserve the semantic information in HINs based on multiple aspects. Instead of preserving information of the network in one semantic space, ASPEM encapsulates information regarding each aspect individually. In order to select aspects for embedding purpose, we further devise a solution for ASPEM based on dataset-wide statistics. To corroborate the efficacy of ASPEM, we conducted experiments on two real-words datasets with two types of applications-classification and link prediction. Experiment results demonstrate that ASPEM can outperform baseline network embedding learning methods by considering multiple aspects, where the aspects can be selected from the given HIN in an unsupervised manner.

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In real-world applications, objects of multiple types are interconnected, forming Heterogeneous Information Networks. In such heterogeneous information networks, we make the key observation that many interactions happen due to some event and the objects in each event form a complete semantic unit. By taking advantage of such a property, we propose a generic framework called HyperEdge-BasedEmbedding (Hebe) to learn object embeddings with events in heterogeneous information networks, where a hyperedge encompasses the objects participating in one event. The Hebe framework models the proximity among objects in each event with two methods: (1) predicting a target object given other participating objects in the event, and (2) predicting if the event can be observed given all the participating objects. Since each hyperedge encapsulates more information of a given event, Hebe is robust to data sparseness and noise. In addition, Hebe is scalable when the data size spirals. Extensive experiments on large-scale real-world datasets show the efficacy and robustness of the proposed framework.

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As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures has always been a fundamental problem and of great pragmatic importance for network mining tasks. Inspired by our probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective. We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods. A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN. We develop an inference algorithm to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks. Experiments on two real-world datasets demonstrate the effectiveness of the proposed model and relevance measure.