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

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5 Author(s)
Kolchinsky, A. ; Sch. of Inf. & Comput., Indiana Univ., Bloomington, IN, USA ; Abi-Haidar, A. ; Kaur, J. ; Hamed, A.A.
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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.

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Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:7 ,  Issue: 3 )