Text Mining to Identify Emerging Trends in Pancreatic Cancer Literature
Dr. Eric GIlbert, Professional Services
About this talk
Text mining is one way to analyze emerging trends in research literature. Here we show how Elsevier’s text mining tool can be used to identify emerging trends using pancreatic cancer as an example. Firstly, trending taxonomy terms are identified by fitting a regression line to the normalized quarterly counts of publications in which the term is found. Then regression parameters are used to filter for terms that have the greatest fold change in publication counts over the time period. The workflow describing the capturing of trending terms will be described in detail. Several captured terms will be highlighted to demonstrate the insights that can be gained in this type of analysis.
About the speaker:
Dr. Eric Gilbert is an accomplished medicinal chemistry research scientist with over 15 years of experience in drug discovery at Pfizer, Schering-Plough, and Merck. He is the author and coauthor of 16 publications and an inventor for 22 issued US patents. Eric possesses a unique combination of synthesis and drug discovery experience along with an extensive data science background.