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Web-based biomedical literature mining
Authors:Jian-fu An  Hui-ping Xue  ying Chen  Jian-guo Wu  Lu Zhang
Institution:[1]Department of Biomedical Engineering, Basic Medical College, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China [2]Division of Gastroenterology and Hepatology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200001, China [3]Department of Nuclear Medicine, Renji HospitM, Shanghai Jiaotong University School of Medicine, Shanghai 200001, China [4]Information and Resource Center, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
Abstract: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.
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