title: Automatic semantic classification of scientific literature according to the hallmarks of cancer.
abstract: BACKGROUND
  The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.
  
  
  RESULTS
  We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.
  
  
  METHODS
  The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html.
  
  
  BACKGROUND
  simon.baker@cl.cam.ac.uk.
authors.affiliation: Computer Laboratory, University of Cambridge, Cambridge, UK.
authors.forename: Simon
authors.initials: S
authors.lastname: Baker
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden and.
authors.forename: Ilona
authors.initials: I
authors.lastname: Silins
authors.affiliation: Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.
authors.forename: Yufan
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden and.
authors.forename: Imran
authors.initials: I
authors.lastname: Ali
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden and.
authors.forename: Johan
authors.initials: J
authors.lastname: Högberg
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden and.
authors.forename: Ulla
authors.initials: U
authors.lastname: Stenius
authors.affiliation: Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.
authors.forename: Anna
authors.initials: A
authors.lastname: Korhonen
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D035843:Biomedical Research; D019295:Computational Biology; D057225:Data Mining / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000473:pathology; D010506:Periodicals as Topic; D039781:PubMed; D012660:Semantics; D012984:Software