There is now more research published than ever before. The primary bibliographic database for biomedical research, PubMed, adds around 3,500 new references every day. Our random sample of 2,000 publications in PubMed suggests that in 2013 there were 98,000 publications describing in vivo experiments, of which 21,000 were in pharmacology and 14,500 in neuroscience. No one individual can read, let alone critically appraise or use even a small fraction of this new information, which is the product of months of investigator effort and substantial investment of research funds. This mismatch between the amount of research produced and the amount that can be effectively used, is a major challenge to biomedical research.
In the SLiM project, we propose to exploit recent developments in text mining and machine learning, and to evaluate their potential to assist with the challenges of systematic reviews of in vivo data outlined above.