Knowledge Graphs and Their Applications in Drug Discovery

James, Tim; Hennig, Holger

doi:10.1007/978-1-0716-3449-3_9

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…Knowledge graphs have been widely used in various fields, such as medicine, network security, journalism, finance, and education [ 10 ]. Knowledge graphs in the biomedical domain have applications in studies related to disease associations [ 11 ], genomics [ 12 ], drug interactions [ 13 ], and support for physicians in formulating individualized treatment regimens [ 14 ]. At present, there are well-established knowledge graphs, including DisGeNET [ 15 ], which integrate information on the associations between genes and diseases; DrugBank [ 16 ], a comprehensive bioinformatics and cheminformatics knowledge base; and ClinVar [ 17 ], a compilation of genetic variation information from diverse laboratories worldwide.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study

Jin,

Liang,

Zhang

et al. 2024

JMIR Med Inform

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Background The incidence of breast cancer has remained high and continues to rise since the 21st century. Consequently, there has been a significant increase in research efforts focused on breast cancer prevention and treatment. Despite the extensive body of literature available on this subject, systematic integration is lacking. To address this issue, knowledge graphs have emerged as a valuable tool. By harnessing their powerful knowledge integration capabilities, knowledge graphs offer a comprehensive and structured approach to understanding breast cancer prevention and treatment. Objective We aim to integrate literature data on breast cancer treatment and prevention, build a knowledge graph, and provide support for clinical decision-making. Methods We used Medical Subject Headings terms to search for clinical trial literature on breast cancer prevention and treatment published on PubMed between 2018 and 2022. We downloaded triplet data from the Semantic MEDLINE Database (SemMedDB) and matched them with the retrieved literature to obtain triplet data for the target articles. We visualized the triplet information using NetworkX for knowledge discovery. Results Within the scope of literature research in the past 5 years, malignant neoplasms appeared most frequently (587/1387, 42.3%). Pharmacotherapy (267/1387, 19.3%) was the primary treatment method, with trastuzumab (209/1805, 11.6%) being the most commonly used therapeutic drug. Through the analysis of the knowledge graph, we have discovered a complex network of relationships between treatment methods, therapeutic drugs, and preventive measures for different types of breast cancer. Conclusions This study constructed a knowledge graph for breast cancer prevention and treatment, which enabled the integration and knowledge discovery of relevant literature in the past 5 years. Researchers can gain insights into treatment methods, drugs, preventive knowledge regarding adverse reactions to treatment, and the associations between different knowledge domains from the graph.

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Section: Introductionmentioning

confidence: 99%

Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study

Jin,

Liang,

Zhang

et al. 2024

JMIR Med Inform

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Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review

Bathula,

Gupta,

Merugu

et al. 2024

Artif Intell Rev

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The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare, addressing critical challenges in securing electronic health records (EHRs), ensuring data privacy, and facilitating secure data transmission. This study provides a comprehensive analysis of the adoption of blockchain and AI within healthcare, spotlighting their role in fortifying security and transparency leading the trajectory for a promising future in the realm of healthcare. Our study, employing the PRISMA model, scrutinized 402 relevant articles, employing a narrative analysis to explore the fusion of blockchain and AI in healthcare. The review includes the architecture of AI and blockchain, examines AI applications with and without blockchain integration, and elucidates the interdependency between AI and blockchain. The major findings include: (i) it protects data transfer, and digital records, and provides security; (ii) enhances EHR security and COVID-19 data transmission, thereby bolstering healthcare efficiency and reliability through precise assessment metrics; (iii) addresses challenges like data security, privacy, and decentralized computing, forming a robust tripod. The fusion of blockchain and AI revolutionize healthcare by securing EHRs, and enhancing privacy, and security. Private blockchain adoption reflects the sector’s commitment to data security, leading to improved efficiency and accessibility. This convergence promises enhanced disease identification, response, and overall healthcare efficacy, and addresses key sector challenges. Further exploration of advanced AI features integrated with blockchain promises to enhance outcomes, shaping the future of global healthcare delivery with guaranteed data security, privacy, and innovation.

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Knowledge Graphs and Their Applications in Drug Discovery

Cited by 2 publications

References 48 publications

Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study

Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study

Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review

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