title: Using unsupervised patterns to extract gene regulation relationships for network construction.
abstract: BACKGROUND
  The gene expression is usually described in the literature as a transcription factor X that regulates the target gene Y. Previously, some studies discovered gene regulations by using information from the biomedical literature and most of them require effort of human annotators to build the training dataset. Moreover, the large amount of textual knowledge recorded in the biomedical literature grows very rapidly, and the creation of manual patterns from literatures becomes more difficult. There is an increasing need to automate the process of establishing patterns.
  
  
  RESULTS
  In this article, we describe an unsupervised pattern generation method called AutoPat. It is a gene expression mining system that can generate unsupervised patterns automatically from a given set of seed patterns. The high scalability and low maintenance cost of the unsupervised patterns could help our system to extract gene expression from PubMed abstracts more precisely and effectively.
  
  
  CONCLUSIONS
  Experiments on several regulators show reasonable precision and recall rates which validate AutoPat's practical applicability. The conducted regulation networks could also be built precisely and effectively. The system in this study is available at http://ikmbio.csie.ncku.edu.tw/AutoPat/.
authors.affiliation: Department of Computer Science and Information Engineering, National Cheng Kung Tainan, Taiwan, Republic of China. p7895125@mail.ncku.edu.tw
authors.forename: Yi-Tsung
authors.initials: YT
authors.lastname: Tang
authors.forename: Shuo-Jang
authors.initials: SJ
authors.lastname: Li
authors.forename: Hung-Yu
authors.initials: HY
authors.lastname: Kao
authors.forename: Shaw-Jenq
authors.initials: SJ
authors.lastname: Tsai
authors.forename: Hei-Chia
authors.initials: HC
authors.lastname: Wang
mesh_terms: D030541:Databases, Genetic; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D051795:Hypoxia-Inducible Factor 1, alpha Subunit / Q000235:genetics; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D015398:Signal Transduction; D016159:Tumor Suppressor Protein p53 / Q000378:metabolism
chemical_list: D051795:Hypoxia-Inducible Factor 1, alpha Subunit; D016159:Tumor Suppressor Protein p53