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Protein-Protein Interaction Document Mining

Protein-protein interactions (PPI) are very important to the understanding of metabolic pathway. Many digital publications are available today; some of them discuss PPI and some of them do not. If machine learning techniques can be used to detect those PPI documents automatically, it would save researchers tremendous amount of time to construct a biological pathway. In this study, we analyze this document mining problem by using different kinds of feature representations and classification algorithms. Latent semantic indexing (LSI) and information gain (IG) were used to extract features from a document for classification, while support vector machine (SVM) and Naive Bayesian (NB) were the selected algorithms. It is found that the combination of LSI and SVM provided the best solution.