Abstract:With the rapid development of natural language processing and text mining technology,
the step of extracting data from literature began changing from manual extraction to automation by
computer. In the past cases, researchers searched entire articles sentence by sentence to looking for key
words or key sentences. But the thorough searching without focus points wasted much time. In thispaper, we took genome-wide association study (GWAS) as the example to develop the strategies of data
automatics extraction for meta-analysis through clearing the positions of data elements we cared about
in the included studies in advance to help computers extract the complete data quickly and accurately
by searching only parts of the literature. At the same time, we used a GWAS study about Alzheimer’s
disease as a case study to search and extract data from all the included studies according to the strategies
that we developed. Results showed that our strategies not only shortened the time of extraction, but also
kept the success rate and accuracy more than 90%. Our research provided effective strategies and a guide
for the research of automatic extraction of GWAS data, which has a promoting effect on the development
of meta-analysis to the big data era.
冀燃,李冬果,张大保. 关于全基因组关联研究的自动化元分析初探[J]. 中国医疗设备, 2017, 32(5): 1-5.
JI Ran, LI Dong-guo,
ZHANG Da-bao. Exploring Automated Meta Analyses of Genome-Wide Association Studies. China Medical Devices, 2017, 32(5): 1-5.