Mining online full-text literature for novel protein interaction discovery

Mining published articles in biology and medicine is a favored means of identifying potential biomarkers in comparison to conventional reviewing process. This is made possible by the development of public literature databases and data mining algorithms. In this article, we present a method to extract novel protein interactions from online full-text articles for biomarker discovery. By evaluating support and confidence metrics, explicit and implicit protein interactions are extracted from corpus of articles. By properly chosen minimum support and confidence, our method maximizes the identification of known interactions while minimizing the number of novel interactions. Hence, our method provides a manageable size of novel interactions for biological validation.