A link prediction approach for drug recommendation in disease-drug bipartite network

Gündoğan, Esra; Kaya, Buket

doi:10.1109/idap.2017.8090219

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…The method is called the internal link method with the base being similarity based link prediction. The results showed that the proposed internal link method had a good percentage of success than other similarity based link prediction methods [8]. In our research, we chose to perform link prediction using collaborative filtering based on community structure.…”

Section: Network Analysis Of Clinical Trial Reportsmentioning

confidence: 99%

“…Network analytics are commonly used to understand structure, development, and relationships of complex systems. Such analysis provides valuable information about the systems, such as link prediction, correlation, or degree distribution [8]. For example, a network based analysis has been used to study long-term collaboration in pharmaceutical industry [5].…”

Section: Network Analysis Of Clinical Trial Studymentioning

confidence: 99%

“…Different from existing research, in this paper, we propose to use bipartite network [8] to represent clinical trial research entities and analyze their relationships. We will create networks to represent infectious disease clinical trials, and understand characteristics of such networks, including most commonly studied areas and community structures of infectious disease clinical trials.…”

Section: Network Analysis Of Clinical Trial Studymentioning

confidence: 99%

“…Bipartite network analysis is commonly used for disease-drug bipartite network. [8]. A study done by Gundogan and Kaya in 2017 discussed a link prediction method in a disease-drug bipartite network.…”

Section: Network Analysis Of Clinical Trial Reportsmentioning

confidence: 99%

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Network Analysis and Recommendation for Infectious Disease Clinical Trial Research

Elkin

Andrews

Zhu

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Clinical trials are crucial for the advancement of treatment and knowledge within the medical community. Since 2007, US federal government took the initiative and required organizations sponsoring clinical trials with at least one site in the United States to submit information on these clinical trials to the ClinicalTrials.gov database, resulting in a rich source of information for clinical trial research. Nevertheless, only a handful of analytic studies have been carried out to understand this valuable data source. In this study, we propose to use network analysis to understand infectious disease clinical trial research. Our goal is to answer two important questions: (1) what are the concentrations and characteristics of infectious disease clinical trial research? and (2) how to accurately predict what type of clinical trials a sponsor (or an investigator) are interested in? The answers to the first question provide effective ways to summarize clinical trial research related to particular disease(s), and the answers to the second question help match clinical trial sponsors and investigators for information recommendation. By using 4,228 clinical trials as the test bed, our study involves 4,864 sponsors and 1,879 research areas characterized by Medical Subject Heading (MeSH) keywords. We extract a set of network measures to show patterns of infectious disease clinical trials, and design a new community based link prediction approach to predict sponsors' interests, with significant improvement compared to baselines. This trans-formative study concludes that using network analysis can tremendously help the understanding of clinical trial research for effective summarization, characterization, and prediction.

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Section: Network Analysis Of Clinical Trial Reportsmentioning

confidence: 99%

Section: Network Analysis Of Clinical Trial Studymentioning

confidence: 99%

Section: Network Analysis Of Clinical Trial Studymentioning

confidence: 99%

Section: Network Analysis Of Clinical Trial Reportsmentioning

confidence: 99%

See 2 more Smart Citations

Network Analysis and Recommendation for Infectious Disease Clinical Trial Research

Elkin

Andrews

Zhu

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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“…Therefore, link prediction plays an important role in research of recommendation system and information spread. [4][5][6][7] Link prediction can be normally used to predict the possibility if a link exists between two unconnected nodes based on the observable network structure. [8][9][10] The problem can be divided into two categories: one is to detect the missing links that should exist in networks, [11][12][13][14] these links may be missed in the process of data acquisition; the other one is to predict the potential links that will become real links in the future.…”

Section: Introductionmentioning

confidence: 99%

Missing link prediction and spurious link detection based on attractive force and community

Chen

Chi

2021

Science Progress

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With the rapid development of Internet and information technology, networks have become an important media of information diffusion in the global. In view of the increasing scale of network data, how to ensure the completeness and accuracy of the obtainable links from networks has been an urgent problem that needs to be solved. Different from most traditional link prediction methods only focus on the missing links, a novel link prediction approach is proposed in this paper to handle both the missing links and the spurious links in networks. At first, we define the attractive force for any pair of nodes to denote the strength of the relation between them. Then, all the nodes can be divided into some communities according to their degrees and the attractive force on them. Next, we define the connection probability for each pair of unconnected nodes to measure the possibility if they are connected, the missing links can be predicted by calculating and comparing the connection probabilities of all the pairs of unconnected nodes. Moreover, we define the break probability for each pair of connected nodes to measure the possibility if they are broken, the spurious links can also be detected by calculating and comparing the break probabilities of all the pairs of connected nodes. To verify the validity of the proposed approach, we conduct experiments on some real-world networks. The results show the proposed approach can achieve higher prediction accuracy and more stable performance compared with some existing methods.

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Smart HealthCare Model: An End-to-end Framework for Disease Prediction and Recommendation of Drugs and Hospitals

Rathi

Jain

Bist

et al. 2020

Studies in Computational Intelligence

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A link prediction approach for drug recommendation in disease-drug bipartite network

Cited by 7 publications

References 18 publications

Network Analysis and Recommendation for Infectious Disease Clinical Trial Research

Network Analysis and Recommendation for Infectious Disease Clinical Trial Research

Missing link prediction and spurious link detection based on attractive force and community

Smart HealthCare Model: An End-to-end Framework for Disease Prediction and Recommendation of Drugs and Hospitals

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