DDI Prediction via Heterogeneous Graph Attention Networks

Polypharmacy, defined as the use of multiple drugs together, is a standard treatment method, especially for severe and chronic diseases. However, using multiple drugs together may cause interactions between drugs. Drug-drug interaction (DDI) is the activity that occurs when the impact of one drug changes when combined with another. DDIs may obstruct, increase, or decrease the intended effect of either drug or, in the worst-case scenario, create adverse side effects. While it is critical to detect DDIs on time, it is timeconsuming and expensive to identify them in clinical trials due to their short duration and many possible drug pairs to be considered for testing. As a result, computational methods are needed for predicting DDIs. In this paper, we present a novel heterogeneous graph attention model, HAN-DDI to predict drug-drug interactions. We create a heterogeneous network of drugs with different biological entities. Then, we develop a heterogeneous graph attention network to learn DDIs using relations of drugs with other entities. It consists of an attention-based heterogeneous graph node encoder for obtaining drug node representations and a decoder for predicting drug-drug interactions. Further, we utilize comprehensive experiments to evaluate of our model and to compare it with state-of-the-art models. Experimental results show that our proposed method, HAN-DDI, outperforms the baselines significantly and accurately predicts DDIs, even for new drugs.Comment: 10 pages, 3 figures, 8 tables, accepted in BioKD

HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network

Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in the worst scenario, they may lead to adverse drug reactions (ADRs). Predicting all DDIs is a challenging and critical problem. Most existing computational models integrate drug-centric information from different sources and leverage them as features in machine learning classifiers to predict DDIs. However, these models have a high chance of failure, especially for the new drugs when all the information is not available. This paper proposes a novel Hypergraph Neural Network (HyGNN) model based on only the SMILES string of drugs, available for any drug, for the DDI prediction problem. To capture the drug similarities, we create a hypergraph from drugs' chemical substructures extracted from the SMILES strings. Then, we develop HyGNN consisting of a novel attention-based hypergraph edge encoder to get the representation of drugs as hyperedges and a decoder to predict the interactions between drug pairs. Furthermore, we conduct extensive experiments to evaluate our model and compare it with several state-of-the-art methods. Experimental results demonstrate that our proposed HyGNN model effectively predicts DDIs and impressively outperforms the baselines with a maximum ROC-AUC and PR-AUC of 97.9% and 98.1%, respectively.Comment: Some new experiments have been added. One more dataset has been considered. Theoretical part has been updated to

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

Clinical and Laboratory Characteristics of Hyperprolactinemia in Children and Adolescents: National Survey

Conclusion: We present the largest cohort of children and adolescents with hyperprolactinemia in the literature to date. Hyperprolactinemia is more common in females and cabergoline is highly effective and practical to use in adolescents, due to its biweekly dosing. Indications for surgery in pediatric cases need to be revised

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Bot Detection on Social Networks Using Persistent Homology

The growth of social media in recent years has contributed to an ever-increasing network of user data in every aspect of life. This volume of generated data is becoming a vital asset for the growth of companies and organizations as a powerful tool to gain insights and make crucial decisions. However, data is not always reliable, since primarily, it can be manipulated and disseminated from unreliable sources. In the field of social network analysis, this problem can be tackled by implementing machine learning models that can learn to classify between humans and bots, which are mostly harmful computer programs exploited to shape public opinions and circulate false information on social media. In this paper, we propose a novel topological feature extraction method for bot detection on social networks. We first create weighted ego networks of each user. We then encode the higher-order topological features of ego networks using persistent homology. Finally, we use these extracted features to train a machine learning model and use that model to classify users as bot vs. human. Our experimental results suggest that using the higher-order topological features coming from persistent homology is promising in bot detection and more effective than using classical graph-theoretic structural features