Web-based biomedical literature mining

With an upsurge in biomedical literature, using data-mining method to search new knowledge from literature has drawing more attention of scholars. In this study, taking the mining of non-coding gene literature from the network database of PubMed as an example, we first preprocessed the abstract data, next applied the term occurrence frequency (TF) and inverse document frequency (IDF) (TF-IDF) method to select features, and then established a biomedical literature data-mining model based on Bayesian algorithm. Finally, we assessed the model through area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, precision rate and recall rate. When 1 000 features are selected, AUC, specificity, sensitivity, accuracy rate, precision rate and recall rate are 0.868 3, 84.63%, 89.02%, 86.83%, 89.02% and 98.14%, respectively. These results indicate that our method can identify the targeted literature related to a particular topic effectively.