Document classification for mining host pathogen protein-protein interactions.
OBJECTIVE
Scientific findings regarding human pathogens and their host responses are buried in the growing volume of biomedical literature and there is an urgent need to mine information pertaining to pathogenesis-related proteins especially host pathogen protein-protein interactions (HP-PPIs) from literature.


METHODS
In this paper, we report our exploration of developing an automated system to identify MEDLINE abstracts referring to HP-PPIs. An annotated corpus consisting of 1360 MEDLINE abstracts was generated. With this corpus, we developed and evaluated document classification systems using support vector machines (SVMs). We also investigated the effects of three feature selection methods:information gain (IG), chi(2) test, and specific mutual information (SI). The performance was measured using normalized discounted cumulative gain (NDCG) and positive predictive value (PPV) and all measures were obtained through 10-fold cross validation.


RESULTS
NDCG measures for classification systems using all features or a subset of features selected using IG and chi(2) test range from 0.83 to 0.89 while classification systems built based on features selected using SI had relatively lower NDCG measures. The classification system achieved a PPV of 50.7% for the top 10% ranked documents comparing to a baseline PPV of 10.0%.


CONCLUSIONS
Our results indicate that document classification systems can be constructed to efficiently retrieve HP-PPI related documents. Feature selection was effective in reducing the dimensionality of features to build a compact system.
Yin
Lanlan
L
Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, USA.
Xu
Guixian
G
Torii
Manabu
M
Niu
Zhendong
Z
Maisog
Jose M
JM
Wu
Cathy
C
Hu
Zhangzhi
Z
Liu
Hongfang
H
D054884:Host-Pathogen Interactions; D006801:Humans; D016247:Information Storage and Retrieval; D011506:Proteins / Q000502:physiology*; D039781:PubMed
D011506:Proteins
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D267-70
14681409
Science. 2006 Jul 14;313(5784):220-3
16840699
BMJ. 1994 Jul 9;309(6947):102
8038641
Expert Rev Proteomics. 2005 Apr;2(2):187-202
15892564
Genome Biol. 2008;9 Suppl 2:S4
18834495
Bioinformatics. 2003;19 Suppl 1:i180-2
12855455
Mol Cell. 2006 Mar 3;21(5):589-94
16507357