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Classification of Protein-Protein Interaction Full-Text Documents Using Text and Citation Network Features
July-September 2010 (vol. 7 no. 3)
pp. 400-411
Artemy Kolchinsky, Indiana University, Bloomington
Alaa Abi-Haidar, Indiana University, Bloomington
Jasleen Kaur, Indiana University, Bloomington
Ahmed Abdeen Hamed, Indiana University, Bloomington
Luis M. Rocha, Indiana University, Bloomington
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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Index Terms:
Text mining, literature mining, binary classification, protein-protein interaction, citation network.
Citation:
Artemy Kolchinsky, Alaa Abi-Haidar, Jasleen Kaur, Ahmed Abdeen Hamed, Luis M. Rocha, "Classification of Protein-Protein Interaction Full-Text Documents Using Text and Citation Network Features," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 3, pp. 400-411, July-Sept. 2010, doi:10.1109/TCBB.2010.55
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