Automating document classification for the Immune Epitope Database

Background: The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose. Results: We here report our experience in automating this process using Naïve Bayes classifiers on 20,910 abstracts classified by domain experts. Improvements on the basic classifier nce were made by a) utilizing information stored in PubMed beyond the abstract itself b) applying standard feature selection criteria and c) extracting domain specific feature patterns that e.g. identify peptides sequences. We have implemented the classifier into the curation process ing if abstracts are clearly relevant, clearly irrelevant, or if no certain classification can be which case the abstracts are manually classified. Testing this classification scheme on an

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Peng Wang, Alexander A. Morgan, Qing Zhang, Alessandro Sette, Björn Peters
• Keywords: Basic Classifier Nce, BMCBI 2007, Immune Epitope Database, Immune Epitopes