title: DTMiner: identification of potential disease targets through biomedical literature mining.
abstract: Biomedical researchers often search through massive catalogues of literature to look for potential relationships between genes and diseases. Given the rapid growth of biomedical literature, automatic relation extraction, a crucial technology in biomedical literature mining, has shown great potential to support research of gene-related diseases. Existing work in this field has produced datasets that are limited both in scale and accuracy.
  
  
  
  In this study, we propose a reliable and efficient framework that takes large biomedical literature repositories as inputs, identifies credible relationships between diseases and genes, and presents possible genes related to a given disease and possible diseases related to a given gene. The framework incorporates name entity recognition (NER), which identifies occurrences of genes and diseases in texts, association detection whereby we extract and evaluate features from gene-disease pairs, and ranking algorithms that estimate how closely the pairs are related. The F1-score of the NER phase is 0.87, which is higher than existing studies. The association detection phase takes drastically less time than previous work while maintaining a comparable F1-score of 0.86. The end-to-end result achieves a 0.259 F1-score for the top 50 genes associated with a disease, which performs better than previous work. In addition, we released a web service for public use of the dataset.
  
  
  
  The implementation of the proposed algorithms is publicly available at http://gdr-web.rwebox.com/public_html/index.php?page=download.php The web service is available at http://gdr-web.rwebox.com/public_html/index.php CONTACT: jenny.wei@astrazeneca.com or kzhu@cs.sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
authors: Department of CSE, Shanghai Jiao Tong University, Shanghai 200240, China.
authors: Dong
authors: D
authors: Xu
authors: R&D Information, Innovation Center China, AstraZeneca, Pudong, Shanghai 201203, China.
authors: Meizhuo
authors: M
authors: Zhang
authors: Department of CSE, Shanghai Jiao Tong University, Shanghai 200240, China.
authors: Yanping
authors: Y
authors: Xie
authors: Department of CSE, Shanghai Jiao Tong University, Shanghai 200240, China.
authors: Fan
authors: F
authors: Wang
authors: R&D Information, Innovation Center China, AstraZeneca, Pudong, Shanghai 201203, China.
authors: Ming
authors: M
authors: Chen
authors: Department of CSE, Shanghai Jiao Tong University, Shanghai 200240, China.
authors: Kenny Q
authors: KQ
authors: Zhu
authors: R&D Information, Innovation Center China, AstraZeneca, Pudong, Shanghai 201203, China.
authors: Jia
authors: J
authors: Wei
mesh_terms: D000465:Algorithms; D035843:Biomedical Research; D057225:Data Mining; D004194:Disease; D015723:Gene Library; D006801:Humans; D010506:Periodicals as Topic