Discovering Content through Text Mining for a Synthetic Biology Knowledge System
Abstract
This paper describes our work using named entity recognition (NER), a sub-field of text mining, to mine existing literature. The goal of NER is to locate and classify named entities present in text into pre-defined categories. For synthetic biology, examples of such categories are names of genes, vectors, and regulatory elements. NER in biology domains has additional challenges due to the pace of new named entities being added, lack of naming convention, lengthy names, presence of special characters, and frequent and variable use of abbreviations.
Type
Publication
IWBDA2020Proceedings
Mai Nguyen
Lead for Data Analytics
My research interests include artificial intelligence, deep/machine learning, and data science.
Gaurav Nakum
Graduate Researcher, M.S.
Xuanyu Wu
Undergraduate Researcher
Bridget McInnes
Assistant Professor
Nicholas Rodriguez
Graduate Researcher, Ph.D.
Eric Young
Assistant Professor
