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.