Extraction of knowledge graph of Covid-19 through mining of unstructured biomedical corpora

Gajendran, Sudhakaran; Manjula, D.; Sugumaran, Vijayan; Hema, R.

doi:10.1016/j.compbiolchem.2022.107808

Cited by 13 publications

(10 citation statements)

References 31 publications

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“…Neural networks (Gajendran et al 2020 , 2023 ) which are a set of algorithms which replicate the cognitive activities in order to identify patterns in large datasets and it extends to different domains. Convolution neural network consists of layers that are minimal specifics and are handled by the training stage, whereas higher-level characteristics are handled by the upcoming levels (Howard et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Securing health care data through blockchain enabled collaborative machine learning

Kumar

Gajendran

Balaji³

et al. 2023

Soft Comput

Self Cite

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Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Securing health care data through blockchain enabled collaborative machine learning

Kumar

Gajendran

Balaji³

et al. 2023

Soft Comput

Self Cite

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“…It can be seen that the morphological and functional information obtained by different imaging technologies in the same anatomy are different and complementary to each other. Therefore, proper integration of various imaging data has become an urgent problem for clinicians [ 19 ].…”

Section: Application Of Medical Image Fusionmentioning

confidence: 99%

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

Peng,

Deng

2024

BMC Med Imaging

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With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot.

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“…Knowledge graphs have gained popularity in recent years as an effective method of structuring and managing large amounts of data. [20] provide a case study on constructing software knowledge graphs, outlining a methodology for acquiring, preprocessing, and constructing knowledge graphs. The authors highlight the importance of evaluating the quality of the knowledge graph through metrics such as precision, recall, and F1 score.…”

Section: Constructionmentioning

confidence: 99%

“…The study by [20] provides valuable insights into constructing software knowledge graphs and offers practical guidance for researchers and practitioners. By outlining a clear methodology for constructing knowledge graphs, the authors provide a framework for structuring and managing copious amounts of data more efficiently and effectively.…”

Section: Constructionmentioning

confidence: 99%

“…By providing recommendations for addressing these challenges, the authors offer practical guidance for researchers and practitioners interested in constructing their own knowledge graphs. Overall, the study by [20] is an important contribution to the field of knowledge graph construction. By outlining a clear methodology for constructing software knowledge graphs and discussing the challenges and recommendations for addressing them, the authors provide valuable insights and practical guidance for researchers and practitioners.…”

Section: Constructionmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Graph: A Survey

Akinnubi,

Ajiboye

2023

Preprint

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This survey discusses the concept of knowledge graphs, including their construction, extraction, and applications. Various tools such as Zotero, Web of Science, Google Scholar, EndNote, and VosViewer are used to analyze and visualize collected data. A Boolean query mechanism ensures the gathered material is relevant to the study. The discussion includes studies on relation extraction using graph neural networks, the application of knowledge graphs in biomedical research, and the use of knowledge graph embedding in healthcare. These studies highlight the growing importance of knowledge graphs in managing and representing complex information. Notable studies discussed include the role of knowledge graphs in connecting related medical information, the application of knowledge graph technology in healthcare, and the potential benefits and limitations of using knowledge graph embedding in biomedical data analysis. This survey paper provides valuable insights into the growing importance of knowledge graphs in managing and representing complex information and how they can help provide new insights into various fields. It suggests potential future directions for research in this area, highlighting the importance of continued exploration and innovation to realize the potential of knowledge graphs fully.

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Extraction of knowledge graph of Covid-19 through mining of unstructured biomedical corpora

Cited by 13 publications

References 31 publications

RETRACTED ARTICLE: Securing health care data through blockchain enabled collaborative machine learning

RETRACTED ARTICLE: Securing health care data through blockchain enabled collaborative machine learning

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

Knowledge Graph: A Survey

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