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Genomics Proteomics Bioinformatics. 2019 Nov 28. pii: S1672-0229(19)30153-6. doi: 10.1016/j.gpb.2018.10.007. [Epub ahead of print]

Translational Informatics for Parkinson's Disease: from Big Biomedical Data to Small Actionable Alterations.

Author information

1
Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu 610041, China. Electronic address: bairong.shen@scu.edu.cn.
2
Center for Systems Biology, Soochow University, Suzhou 215006, China.
3
Center for Translational Biomedical Informatics, Guizhou University School of Medicine, Guiyang 550025, China.

Abstract

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.

KEYWORDS:

Disease biomarker; Healthcare; Parkinson's disease; Systems modelling; Translational informatics

PMID:
31786313
DOI:
10.1016/j.gpb.2018.10.007
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