Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases

Yang, Christopher C.; Zhao, Mengnan

doi:10.1016/j.artmed.2019.03.003

Cited by 15 publications

(7 citation statements)

References 47 publications

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“…Yang and other scholars proposed a secret Internet protocol. When using this protocol, the secret protocol can be randomly embedded in the VoIP protocol of the self-media system, so as to protect privacy [12]. Max and other scholars optimized a self-media protection system, which has the functions of system index, logical database, and query structure, which can be used to optimize the self-media privacy protection methods [13].…”

Section: Related Workmentioning

confidence: 99%

Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

2022

Wireless Communications and Mobile Computing

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In recent years, with the in-depth research on the privacy protection methods of We-media network, various types of big data analysis technologies gradually protect the network privacy data, but there are still problems of low intelligence and poor protection in the existing research. Based on this, this paper first uses big data technology and artificial intelligence deep learning algorithm to complete the construction of different types of self-media control databases. Then, it analyzes the common privacy data types of We-media network, constructs an optimization protection model based on secondary identification and verification strategy, and forms a data query system. Finally, simulation experiments are conducted to verify whether the constructed network privacy protection model can realize the intelligent protection of network privacy algorithms from different dimensions. In the process of privacy protection of experimental data at different stages, the internal correlation differences of different types of protection algorithm strategies are obvious in the multidimensional analysis of specific databases. For different types of factor data in different types of We-media networks, the protection rate of the We-media network privacy protection model designed in this study has reached more than 95%. The research results show that the self-media network privacy protection model based on big data and artificial intelligence deep learning technology can realize protection from the aspects of gateway verification and data encryption and has high accuracy and reliability.

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Section: Related Workmentioning

confidence: 99%

Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

2022

Wireless Communications and Mobile Computing

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“…Yang et al [127] proposed a new systematic model to identify therapeutic potentials and drug repositioning candidates in heterogeneous networks. The authors combined molecular data, side effects, and online health community information to construct a heterogeneous network that consists of drugs, diseases, and adverse drug reactions as intermediates.…”

Section: Network Centrality Measuresmentioning

confidence: 99%

“…Yang et al [127] used scientific articles published by PubMed as a medical literature cross-referencing model to evaluate the performance of their proposed models. Furthermore, the authors consulted medical experts to evaluate their findings and guarantee the accuracy of their proposed model.…”

Section: Validation Of Computational Drug Repositioning Modelsmentioning

confidence: 99%

A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions

2020

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Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.

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“…Zhao and Yang [18] proposed the tensor decomposition and the meta-path method in heterogeneous networking for off-label drug use detecting with health consumer-generated data. Yang and Zhao [19] used the phenotypic information extracted from pharmaceutical databases and social media data and heterogeneous networking mining to identify potential drugs for repositioning.…”

Section: Examples Of Predictive Modeling In Healthcare With Different...mentioning

confidence: 99%

Explainable Artificial Intelligence for Predictive Modeling in Healthcare

Yang

2022

J Healthc Inform Res

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The principle behind artificial intelligence is mimicking human intelligence in the way that it can perform tasks, recognize patterns, or predict outcomes through learning from the acquired data of various sources. Artificial intelligence and machine learning algorithms have been widely used in autonomous driving, recommender systems in electronic commerce and social media, fintech, natural language understanding, and question answering systems. Artificial intelligence is also gradually changing the landscape of healthcare research 25). The rule-based approach that relied on the curation of medical knowledge and the construction of robust decision rules had drawn significant attention in diagnosing diseases and clinical decision support since half a century ago. In recent years, machine learning algorithms such as deep learning that can account for complex interactions between features is shown to be promising in predictive modeling in healthcare (Deo in Circulation 132:1920-1930. Although many of these artificial intelligence and machine learning algorithms can achieve remarkably high performance, it is often difficult to be completely adopted in practical clinical environments due to the lack of explainability in some of these algorithms. Explainable artificial intelligence (XAI) is emerging to assist in the communication of internal decisions, behavior, and actions to health care professionals. Through explaining the prediction outcomes, XAI gains the trust of the clinicians as they may learn how to apply the predictive modeling in practical situations instead of blindly following the predictions. There are still many scenarios to explore how to make XAI effective in clinical settings due to the complexity of medical knowledge.

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Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases

Cited by 15 publications

References 47 publications

Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions

Explainable Artificial Intelligence for Predictive Modeling in Healthcare

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