Raja Kalpana, Patrick Matthew, Gao Yilin, Madu Desmond, Yang Yuyang, Tsoi Lam C
Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA.
Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Int J Genomics. 2017;2017:6213474. doi: 10.1155/2017/6213474. Epub 2017 Feb 26.
In the past decade, the volume of "omics" data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.
在过去十年中，不同高通量技术产生的“组学”数据量呈指数级增长。对这些大数据进行管理、存储和分析，对研究人员来说是一项巨大挑战，尤其是在朝着生成可检验的数据驱动假设这一目标迈进时，而这正是高通量实验技术所带来的希望。人们已开发出不同的生物信息学方法，通过提供独立信息来解释和进行生物学推断，从而简化下游分析。文本挖掘（也称为文献挖掘）是从大量已发表文章中自动生成生物学知识的常用方法之一。在这篇综述论文中，我们讨论了将组学数据结果与文本挖掘方法生成的信息相结合以发现新生物医学信息的方法的最新进展。
