Quer analisar milhões de artigos científicos de uma vez? Seus problemas acabaram!!!

Text mining of 15 million full-text scientific articles

David Westergaard, Hans-Henrik Stærfeldt, Christian Tønsberg, Lars Juhl Jensen,  Søren Brunak

doi: https://doi.org/10.1101/162099

This article is a preprint and has not been peer-reviewed [what does this mean?].

Source/Fonte

Abstract

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

Copyright 

The copyright holder for this preprint is the author/funder. It is made available under a CC-BY 4.0 International license.

FREE PDF GRATIS: bioRxiv