Rani Jyoti, Shah A B Rauf, Ramachandran Srinivasan
GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, New Delhi 110 025, India.
J Biosci. 2015 Oct;40(4):671-82. doi: 10.1007/s12038-015-9552-2.
The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, 'Evolving role of diabetes educators', 'Cancer risk assessment' and 'Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.rproject. org/web/packages/pubmed.mineR.
PubMed文献数据库是科学研究的宝贵信息来源。它拥有丰富的生物医学文献，引用次数超过2400万次。对大量文献进行数据挖掘是一项具有挑战性的任务。尽管近年来已经开发了几种侧重于数据可视化的文本挖掘算法，但它们存在速度慢、过于僵化以及不开源等局限性。我们开发了一个R包pubmed.mineR，在其中我们结合了现有算法的优点，克服了它们的局限性，并为用户提供灵活性，以及与生物导体（Bioconductor）和综合R网络（CRAN）中的其他包建立链接，以扩展用户执行多方面方法的能力。本文展示了三个案例研究，即“糖尿病教育者角色的演变”、“癌症风险评估”和“疾病与共病的动态概念”，以说明pubmed.mineR的使用。即使处理大型语料库和计算密集型函数，该包在常规工作站上通常运行速度很快，耗时较短。pubmed.mineR可在http://cran.rproject.org/web/packages/pubmed.mineR获取。
