Knowledge graph application in rare diseases: a scoping review_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Xin , CUI Wenjin , YAO Jiarui , ZHANG Yaya ,  WANG Yanhong

School of Nursing, Lanzhou University, Lanzhou 730011, P. R. China;

Corresponding?author：

WANG Yanhong, Email: yanhongwang@lzu.edu.cn

Keywords：

Knowledge graph; Rare diseases; Scoping review

DOI：

10.7507/1672-2531.202306148

Video：

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Objective To conduct a scoping review of studies on the application of knowledge mapping in the field of rare diseases at home and abroad, in order to clarify the content and status of application and provide references for future research in this field. Methods Relevant studies in PubMed, Web of Science, Embase, MEDLINE, CNKI, WanFang Data, VIP, and CBM databases were searched, using the Joanna Briggs Institute Scoping Review Guidelines in Australia as the methodological framework, and the search time frame was from the establishment of the database to June 1, 2023. Results Twenty-five papers were included, and the main applications of knowledge graphs in the field of rare diseases were knowledge management, assisted diagnosis, drug repositioning and decision support, involving techniques such as knowledge representation, knowledge extraction, knowledge reasoning, knowledge fusion and knowledge storage.Conclusion Knowledge graphs have shown positive results in fusing and exploiting multi-source information, aiding disease prediction and diagnosis and drug development, but further technical improvements are needed.

Citation： ZHANG Xin, CUI Wenjin, YAO Jiarui, ZHANG Yaya, WANG Yanhong. Knowledge graph application in rare diseases: a scoping review. Chinese Journal of Evidence-Based Medicine, 2023, 23(12): 1428-1433. doi: 10.7507/1672-2531.202306148 Copy

1.	陳園園, 王萍. 罕見病患者權利的缺失與保護. 醫學與社會, 2016, 29(10): 68-70.
2.	Lu Y, Han J. The definition of rare disease in China and its prospects. Intractable Rare Dis Res, 2022, 11(1): 29-30.
3.	Nguengang Wakap S, Lambert DM, Olry A, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet, 2020, 28(2): 165-173.
4.	Chung CCY, Hong Kong Genome Project, Chu ATW, et al. Rare disease emerging as a global public health priority. Front Public Health, 2022, 10: 1028545.
5.	李瑩. 關于我國罕見病相關政策制定的探討—基于罕見病群體生活狀況調研的分析. 中國軟科學, 2014, 278(2): 77-89.
6.	黃恒琪, 于娟, 廖曉, 等. 知識圖譜研究綜述. 計算機系統應用, 2019, 28(6): 1-12.
7.	梁靜, 文奕. 知識圖譜在醫學輔助診斷中的應用研究. 醫學信息學雜志, 2022, 43(11): 34-40.
8.	王喜益, 葉志弘, 湯磊雯. 范圍綜述在護理領域的應用進展. 中華護理雜志, 2019, 54(8): 1259-1263.
9.	Lockwood C, Dos Santos KB, Pap R. Practical guidance for knowledge synthesis: scoping review methods. Asian Nurs Res (Korean Soc Nurs Sci), 2019, 13(5): 287-294.
10.	Seo H, Kim D, Chae JH, et al. Development of Korean rare disease knowledge base. Healthc Inform Res, 2012, 18(4): 272-278.
11.	Lopes P, Oliveira JL. An innovative portal for rare genetic diseases research: the semantic diseasecard. J Biomed Inform, 2013, 46(6): 1108-1115.
12.	Choquet R, Maaroufi M, Fonjallaz Y, et al. LORD: a phenotype-genotype semantically integrated biomedical data tool to support rare disease diagnosis coding in health information systems. AMIA Annu Symp Proc. 2015, 11(5): 434-440.
13.	Pinol M, Alves R, Teixido I, et al. Rare disease discovery: an optimized disease ranking system. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1184-1192.
14.	Sernadela P, González-Castro L, Carta C, et al. Linked registries: connecting rare diseases patient registries through a semantic web layer. Biomed Res Int, 2017, 2017: 8327980.
15.	Adler A, Kirchmeier P, Reinhard J, et al. PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases. Orphanet J Rare Dis, 2018, 13(1): 22.
16.	Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med, 2019, 25(1): 60-64.
17.	Li X, Wang Y, Wang D, et al. Improving rare disease classification using imperfect knowledge graph. BMC Med Inform Decis Mak, 2019, 19(5): 238.
18.	Subirats L, Conesa J, Armayones M. Biomedical holistic ontology for people with rare diseases. Int J Environ Res Public Health, 2020, 17(17): 6038.
19.	Zhu Q, Nguyen DT, Grishagin I, et al. An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD). J Biomed Semantics, 2020, 11(1): 13.
20.	Latorre-Pellicer A, Ascaso á, Trujillano L, et al. Evaluating face2gene as a tool to identify cornelia de lange syndrome by facial phenotypes. Int J Mol Sci, 2020, 21(3): 1042.
21.	Zhu Q, Nguyen DT, Alyea G, et al. Phenotypically similar rare disease identification from an integrative knowledge graph for data harmonization: preliminary study. JMIR Med Inform, 2020, 8(10): e18395.
22.	陳一龍, 卜嘉彬, 李景宇, 等. 基于知識圖譜的罕見病就醫決策引擎設計研究. 華西醫學, 2021, 36(12): 1730-1733.
23.	Zhu Q, Nguy?n DT, Sheils T, et al. Scientific evidence based rare disease research discovery with research funding data in knowledge graph. Orphanet J Rare Dis, 2021, 16(1): 483.
24.	Yang J, Dong C, Duan H, et al. RDmap: a map for exploring rare diseases. Orphanet J Rare Dis, 2021, 16(1): 101.
25.	程世成. 智能藥物重定向與疾病輔助決策關鍵技術研究. 大連: 大連理工大學, 2021.
26.	Havrilla JM, Liu C, Dong X, et al. PhenCards: a data resource linking human phenotype information to biomedical knowledge. Genome Med, 2021, 13(1): 91.
27.	Sun Z, Yin H, Chen H, et al. Disease prediction via graph neural networks. IEEE J Biomed Health Inform, 2021, 25(3): 818-826.
28.	Alves VM, Korn D, Pervitsky V, et al. Knowledge-based approaches to drug discovery for rare diseases. Drug Discov Today, 2022, 27(2): 490-502.
29.	Kuo TC, Wang PH, Wang YK, et al. RSDB: a rare skin disease database to link drugs with potential drug targets for rare skin diseases. Sci Data, 2022, 9(1): 521.
30.	Zhu Q, Qu C, Liu R, et al. Rare disease-based scientific annotation knowledge graph. Front Artif Intell, 2022, 5: 932665.
31.	Foksinska A, Crowder CM, Crouse AB, et al. The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren. Front Artif Intell, 2022, 5: 910216.
32.	Ma C, Zhou Z, Liu H, et al. KGML-xDTD: a knowledge graph-based machine learning framework for drug treatment prediction and mechanism description. Gigascience, 2022, 12: giad057.
33.	Martínez-deMiguel C, Segura-Bedmar I, Chacón-Solano E, et al. The RareDis corpus: a corpus annotated with rare diseases, their signs and symptoms. J Biomed Inform, 2022, 125: 103961.
34.	Sanjak J, Zhu Q, Math EA. Clustering rare diseases within an ontology-enriched knowledge graph. bioRxiv [Preprint], 2023, (2): 2023.02. 15.528673.
35.	趙廣立, 李惠鈺. AI“望診”能知罕見病. 商業觀察, 2019, (Z1): 30-32.

1. 陳園園, 王萍. 罕見病患者權利的缺失與保護. 醫學與社會, 2016, 29(10): 68-70.
2. Lu Y, Han J. The definition of rare disease in China and its prospects. Intractable Rare Dis Res, 2022, 11(1): 29-30.
3. Nguengang Wakap S, Lambert DM, Olry A, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet, 2020, 28(2): 165-173.
4. Chung CCY, Hong Kong Genome Project, Chu ATW, et al. Rare disease emerging as a global public health priority. Front Public Health, 2022, 10: 1028545.
5. 李瑩. 關于我國罕見病相關政策制定的探討—基于罕見病群體生活狀況調研的分析. 中國軟科學, 2014, 278(2): 77-89.
6. 黃恒琪, 于娟, 廖曉, 等. 知識圖譜研究綜述. 計算機系統應用, 2019, 28(6): 1-12.
7. 梁靜, 文奕. 知識圖譜在醫學輔助診斷中的應用研究. 醫學信息學雜志, 2022, 43(11): 34-40.
8. 王喜益, 葉志弘, 湯磊雯. 范圍綜述在護理領域的應用進展. 中華護理雜志, 2019, 54(8): 1259-1263.
9. Lockwood C, Dos Santos KB, Pap R. Practical guidance for knowledge synthesis: scoping review methods. Asian Nurs Res (Korean Soc Nurs Sci), 2019, 13(5): 287-294.
10. Seo H, Kim D, Chae JH, et al. Development of Korean rare disease knowledge base. Healthc Inform Res, 2012, 18(4): 272-278.
11. Lopes P, Oliveira JL. An innovative portal for rare genetic diseases research: the semantic diseasecard. J Biomed Inform, 2013, 46(6): 1108-1115.
12. Choquet R, Maaroufi M, Fonjallaz Y, et al. LORD: a phenotype-genotype semantically integrated biomedical data tool to support rare disease diagnosis coding in health information systems. AMIA Annu Symp Proc. 2015, 11(5): 434-440.
13. Pinol M, Alves R, Teixido I, et al. Rare disease discovery: an optimized disease ranking system. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1184-1192.
14. Sernadela P, González-Castro L, Carta C, et al. Linked registries: connecting rare diseases patient registries through a semantic web layer. Biomed Res Int, 2017, 2017: 8327980.
15. Adler A, Kirchmeier P, Reinhard J, et al. PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases. Orphanet J Rare Dis, 2018, 13(1): 22.
16. Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med, 2019, 25(1): 60-64.
17. Li X, Wang Y, Wang D, et al. Improving rare disease classification using imperfect knowledge graph. BMC Med Inform Decis Mak, 2019, 19(5): 238.
18. Subirats L, Conesa J, Armayones M. Biomedical holistic ontology for people with rare diseases. Int J Environ Res Public Health, 2020, 17(17): 6038.
19. Zhu Q, Nguyen DT, Grishagin I, et al. An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD). J Biomed Semantics, 2020, 11(1): 13.
20. Latorre-Pellicer A, Ascaso á, Trujillano L, et al. Evaluating face2gene as a tool to identify cornelia de lange syndrome by facial phenotypes. Int J Mol Sci, 2020, 21(3): 1042.
21. Zhu Q, Nguyen DT, Alyea G, et al. Phenotypically similar rare disease identification from an integrative knowledge graph for data harmonization: preliminary study. JMIR Med Inform, 2020, 8(10): e18395.
22. 陳一龍, 卜嘉彬, 李景宇, 等. 基于知識圖譜的罕見病就醫決策引擎設計研究. 華西醫學, 2021, 36(12): 1730-1733.
23. Zhu Q, Nguy?n DT, Sheils T, et al. Scientific evidence based rare disease research discovery with research funding data in knowledge graph. Orphanet J Rare Dis, 2021, 16(1): 483.
24. Yang J, Dong C, Duan H, et al. RDmap: a map for exploring rare diseases. Orphanet J Rare Dis, 2021, 16(1): 101.
25. 程世成. 智能藥物重定向與疾病輔助決策關鍵技術研究. 大連: 大連理工大學, 2021.
26. Havrilla JM, Liu C, Dong X, et al. PhenCards: a data resource linking human phenotype information to biomedical knowledge. Genome Med, 2021, 13(1): 91.
27. Sun Z, Yin H, Chen H, et al. Disease prediction via graph neural networks. IEEE J Biomed Health Inform, 2021, 25(3): 818-826.
28. Alves VM, Korn D, Pervitsky V, et al. Knowledge-based approaches to drug discovery for rare diseases. Drug Discov Today, 2022, 27(2): 490-502.
29. Kuo TC, Wang PH, Wang YK, et al. RSDB: a rare skin disease database to link drugs with potential drug targets for rare skin diseases. Sci Data, 2022, 9(1): 521.
30. Zhu Q, Qu C, Liu R, et al. Rare disease-based scientific annotation knowledge graph. Front Artif Intell, 2022, 5: 932665.
31. Foksinska A, Crowder CM, Crouse AB, et al. The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren. Front Artif Intell, 2022, 5: 910216.
32. Ma C, Zhou Z, Liu H, et al. KGML-xDTD: a knowledge graph-based machine learning framework for drug treatment prediction and mechanism description. Gigascience, 2022, 12: giad057.
33. Martínez-deMiguel C, Segura-Bedmar I, Chacón-Solano E, et al. The RareDis corpus: a corpus annotated with rare diseases, their signs and symptoms. J Biomed Inform, 2022, 125: 103961.
34. Sanjak J, Zhu Q, Math EA. Clustering rare diseases within an ontology-enriched knowledge graph. bioRxiv [Preprint], 2023, (2): 2023.02. 15.528673.
35. 趙廣立, 李惠鈺. AI“望診”能知罕見病. 商業觀察, 2019, (Z1): 30-32.

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