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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (1): 1-8    DOI: 10.11925/infotech.2096-3467.2017.1330
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Biomedical Informatics Studies for Knowledge Discovery in Precision Medicine
Zhang Zhiqiang1(), Fan Shaoping2, Chen Xiujuan1,3
1(Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China)
2(Institute of Medical Information & Library, Chinese Academy of Medical Sciences, Beijing 100020, China)
3(University of Chinese Academy of Sciences, Beijing 100049, China)
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Abstract  

[Objective] This paper reviews the latest Biomedical Informatics studies and indicates some future directions for data-driven knowledge discovery in precision medicine. [Methods] We summarized the developments of data resources, data analysis platforms and methods, clinical decision-making applications in Biomedical Informatics through literature review and service trials. [Results] Future directions of Biomedical Informatics include building better big data management system, proposing theories and methods for big data analysis, developing new tools and platforms, clinical application of research findings, as well as training senior personnel. [Limitations] More biomedical data resources, methods, and case studies should be added. [Conclusions] This study identifies the future developments of Biomedical Informatics in precision medicine, which utilizes big data analytics to discover more knowledge.

Key wordsPrecision Medicine      Biomedical Informatics      Data Resources Construction      Knowledge Discovery      Data Platform      Clinical Decision Application     
Received: 27 December 2017      Published: 05 February 2018
ZTFLH:  G350  

Cite this article:

Zhang Zhiqiang,Fan Shaoping,Chen Xiujuan. Biomedical Informatics Studies for Knowledge Discovery in Precision Medicine. Data Analysis and Knowledge Discovery, 2018, 2(1): 1-8.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1330     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I1/1

数据库名称 数据量 年增长量
PubMed 26 413 966 4.7%
MeSH 265 382 2.4%
ClinVar 159 184 27.4%
dbVar 6 147 903 37.2%
SNP 819 309 474 16.1%
Taxonomy 1 617 350 13.3%
Gene 24 351 351 13.8%
Protein 307 799 547 37.7%
PubChem Compound 91 679 397 50.9%
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