It has become clear that text mining is a great tool for rapid analysis of large volumes of biomedical literature. It delivers valuable information that helps to improve the accuracy and speed of the R&D process, as well as creating the opportunity to make more informed business decisions. As we’ve seen in our previous articles, text mining is clearly the tool to revolutionize Big Data!
An important issue encountered in text mining is that in many cases, the search function looks at freely available abstracts, extracted from databases such as PubMed. Many scientists that use text-mining are therefore running analyses on abstracts of the article rather than on the full text version.
And there might be a problem there… While it is true that abstracts contain valuable sections of information, there are limitations. A brief example is that the ‘materials & methods’ section is missing, which dramatically affects the final results of text mining.
With RightsDirect and its XML for Mining tool,