@Article{Dopp_Morrone_Kahanda_2017,
  author       = {Dopp, Daniel and Morrone, Adam and Kahanda, Indika},
  title        = {Kin{DER}: A Biocuration Tool for Extracting Kinase Knowledge from Biomedical Literature},
  journal      = {Proceedings of the BioCreative VI Workshop},
  year         = {2017},
  month        = {Oct},
  url          = {http://par.nsf.gov/biblio/10046547},
  abstractnote = {Kinases are enzymes that mediate phosphate transfer. Extracting information on kinases from biomedical literature is an important task which has direct implications for applications such as drug design. In this work, we develop KinDER, Kinase Document Extractor and Ranker, a biomedical natural language processing tool for extracting functional and disease related information on kinases. This tool combines information retrieval and machine learning techniques to automatically extract information about protein kinases. First, it uses several bio-ontologies to retrieve documents related to kinases and then uses a supervised classification model to rank them according to their relevance. This was developed to participate in the Text-mining services for Human Kinome Curation Track of the BioCreative VI challenge. According to the official BioCreative evaluation results, KinDER provides stateof- the-art performance for extracting functional information on kinases from abstracts.},
  place        = {Country unknown/Code not available},
}

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