Classification of Protein-Protein Interaction Full-Text Documents Using Text and Citation Network Features

Abstract--We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of fulltext documents relevant for protein-protein interaction. We used two distinct classifiers for the online and offline challenges: (1) the lightweight Variable Trigonometric Threshold (VTT) linear classifier we successfully introduced in BioCreative 2 for binary classification acts, and (2) a novel Naive Bayes classifier using features from the citation network of the relevant literature. We supplemented the supplied training data with full-text documents from the MIPS database. The lightweight VTT classifier was very competitive in this new full-text scenario: it was a top performing submission in this task, taking into account the rank product of the Area Under the interpolated precision and recall Curve, Accuracy, Balanced F-Score, and Matthew's Correlation Coefficient performance measures. The novel citation network classifier for the biomedica...

• Type: Journal
• Year: 2010
• Where: TCBB
• Authors: Artemy Kolchinsky, Alaa Abi-Haidar, Jasleen Kaur, Ahmed Abdeen Hamed, Luis Mateus Rocha
• Keywords: Binary Classification, Citation Network, Naive Bayes Classifier, Software Engineering, TCBB 2010