According to the Hallmarks of Cancer

Motivation: The hallmarks of cancer by Hanahan and Weinberg (2000, 2011) have become highly influential in cancer research. They reduce the complexity of cancer into ten principles (e.g. resisting cell death, sustaining proliferative signaling) that explain the biological capabili-ties acquired during the development of human tumours. Since new research depends crucially on existing knowledge, technology for se-mantic 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 towards the development of such technology. We introduce a corpus of 1,499 PubMed abstracts an-notated according to the scientific evidence they provide for the ten 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 ac-curacy 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. Availability: The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at