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. 2022 Sep:40:223-231.
doi: 10.1016/j.jare.2021.06.015. Epub 2021 Jun 20.

NDDRF: A risk factor knowledgebase for personalized prevention of neurodegenerative diseases

Affiliations

NDDRF: A risk factor knowledgebase for personalized prevention of neurodegenerative diseases

Cheng Bi et al. J Adv Res. 2022 Sep.

Abstract

Introduction: Neurodegenerative diseases (NDDs) are a series of chronic diseases, which are associated with progressive loss of neuronal structure or function. The complex etiologies of the NDDs remain unclear, thus the prevention and early diagnosis of NDDs are critical to reducing the mortality and morbidity of these diseases.

Objectives: To provide a systematic understanding of the heterogeneity of the risk factors associated with different NDDs (pan-neurodegenerative diseases or pan-NDDs), the knowledgebase is established to facilitate the personalized and knowledge-guided diagnosis, prevention and prediction of NDDs.

Methods: Before data collection, the medical, lifescienceand informatics experts as well as the potential users of the database were consulted and discussed for the scope of data and the classification of risk factors. The PubMed database was used as the resource of the data and knowledge extraction. Risk factors of NDDs were manually collected from literature published between 1975 and 2020.

Results: The comprehensive risk factors database for NDDs (NDDRF) was established including 998 single or combined risk factors, 2293 records and 1071 articles relevant to the 14 most common NDDs. The single risk factors are classified into 3 categories, i.e. epidemiological factors (469), genetic factors (324) and biochemical factors (153). Among all the factors, 179 factors are positive and protective, while 880 factors have negative influence for NDDs. The knowledgebase is available at http://sysbio.org.cn/NDDRF/.

Conclusion: NDDRF provides the structured information and knowledge resource on risk factors of NDDs. It could benefit the future systematic and personalized investigation of pan-NDDs genesis and progression. Meanwhile it may be used for the future explainable artificial intelligence modeling for smart diagnosis and prevention of NDDs.

Keywords: Diagnosis and prevention; Knowledge base; Neurodegenerative diseases; Protective factor; Risk factor.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Flowchart for collection of risk factors for neurodegenerative diseases.
Fig. 2
Fig. 2
Unified modeling language class diagrams of NDDRF. Note: N (allowed to be Null); U (Unique key).
Fig. 3
Fig. 3
Web interface of NDDRF. (A) Risk factor page; (B) Categories of NDD; (C) Categories of risk factors; (D) Search page; (E) Details of risk factor; (F) Advanced search; (G) Submission page.
Fig. 4
Fig. 4
Descriptive statistics for NDDRF. (A) Chart shows the number of studies by year of publication; (B) Chart shows categories of risk factors; (C) Chart shows the association of risk factors for all 14 NDDs; (D) Chart shows the records of risk factors by disease in NDDRF; (E) Heat map shows country distribution; (F) Weighted network diagram shows correlation between neurodegenerative diseases and risk factors. The nodes represent NDDs and risk factors, and number for nodes indicates the risk factor ID in the NDDRF. The size of the node for each NDD indicates the number of associated risk factors and the size of node for each risk factor represents the number of associated NDDs. The thickness and depth of the edges represent connectivity. The edge of the solid line represents risk factors and the dotted line represents protective factors. The RF nodes are colored black, red and blue to represent epidemiological, genetic and biochemical factors.

References

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