Paper Info
Title
PathText: a text mining integrator for biological pathway visualizations.
Abstract
Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations. Contact: brian@monrovian.com.
Year
DOI
Venue
2010
10.1093/bioinformatics/btq221
BIOINFORMATICS
Keywords
Field
DocType
text mining
Data science,Data mining,Scientific literature,Text mining,Annotation,Computer science,Integrator,Systems biology,Software,Bioinformatics
Journal
Volume
Issue
ISSN
26
12
1367-4803
Citations 
PageRank 
References 
22
0.75
22
Authors
7
Name
Order
Citations
PageRank
Brian Kemper1221.08
Takuya Matsuzaki274042.47
Yukiko Matsuoka324515.09
Yoshimasa Tsuruoka4136488.95
Hiroaki Kitano53515539.37
Sophia Ananiadou62658183.08
Jun-ichi Tsujii71973219.85