Improving protein-protein interaction article classification using biological domain knowledge.
Interaction Article Classification (IAC) is a specific text classification application in biological domain that tries to find out which articles describe Protein-Protein Interactions (PPIs) to help extract PPIs from biological literature more efficiently. However, the existing text representation and feature weighting schemes commonly used for text classification are not well suited for IAC. We capture and utilise biological domain knowledge, i.e. gene mentions also known as protein or gene names in the articles, to address the problem. We put forward a new gene mention order-based approach that highlights the important role of gene mentions to represent the texts. Furthermore, we also incorporate the information concerning gene mentions into a novel feature weighting scheme called Gene Mention-based Term Frequency (GMTF). By conducting experiments, we show that using the proposed representation and weighting schemes, our Interaction Article Classifier (IACer) performs better than other leading systems for the moment.
Yifei
Y
Chen
Hongjian
H
Guo
Feng
F
Liu
Bernard
B
Manderick
D000818:Animals; D057225:Data Mining / Q000379:methods*; D006801:Humans; D010506:Periodicals as Topic; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*
D011506:Proteins