Abstract
This is a first study attempting to suggest an automatic categorization of microRNA articles from PubMed. In this study, text classification techniques using binary representations were applied on the abstract section of the articles. The PubMed articles related to microRNA targets were regarded as the positive class whereas documents retrieved using different criteria are used as a negative class. The results show that with a careful choice of the negative class, the PubMed articles about microRNA targets can be accurately distinguished. Moreover, we showed the robustness of the automatic text classification by building models not just from the top keywords but also from a combination of other keywords.
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