Classification of protein-protein interaction full-text documents using text and citation network features.
We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of full-text 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 of abstracts 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 biomedical text mining domain, while not a top performing classifier in the challenge, performed above the central tendency of all submissions, and therefore indicates a promising new avenue to investigate further in bibliome informatics.
Kolchinsky
Artemy
A
School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA. akolchin@indiana.edu
Abi-Haidar
Alaa
A
Kaur
Jasleen
J
Hamed
Ahmed Abdeen
AA
Rocha
Luis M
LM
D000043:Abstracting and Indexing / Q000145:classification*; D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016206:Databases, Bibliographic; D016571:Neural Networks, Computer; D010506:Periodicals as Topic; D025941:Protein Interaction Mapping / Q000145:classification*