Department of Cell Biology, Harvard Medical School, Boston, United States; Department of Systems Biology, Harvard Medical School, Boston, United States; Department of Informatics, Technische Universität München, Garching, Germany
Department of Cell Biology, Harvard Medical School, Boston, United States; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States; Broad Institute, Massachusetts Institute of Technology, Harvard University, Boston, United States
The Donnelly Centre, University of Toronto, Toronto, Canada; Department of Computer Science, Department of Molecular Genetics, University of Toronto, Toronto, United States; The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States; Broad Institute, Massachusetts Institute of Technology, Harvard University, Boston, United States
Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.
