bioRxiv preprint doi: https://doi.org/10.1101/2020.10.16.343012; this version posted October 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. SKiM - A generalized literature-based discovery system for uncovering novel biomedical knowledge from PubMed Kalpana Raja1, John Steill1, Ian Ross2, Lam C Tsoi3,4,5, Finn Kuusisto1, Zijian Ni6, Miron Livny2, James Thomson1,7 and Ron Stewart1* 1Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA 2Center for High Throughput Computing, Computer Sciences Department, University of Wisconsin, Madison, WI, USA 3Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA 4Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA 5Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA 6Department of Statistics, University of Wisconsin, Madison, WI, USA 7Department of Cell and Regenerative Biology, University of Wisconsin, Madison, WI, USA *Corresponding author Email:
[email protected] Phone: (608) 316-4349 Home page: https://morgridge.org/profile/ron-stewart/ bioRxiv preprint doi: https://doi.org/10.1101/2020.10.16.343012; this version posted October 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Literature-based discovery (LBD) uncovers undiscovered public knowledge by linking terms A to C via a B intermediate. Existing LBD systems are limited to process certain A, B, and C terms, and many are not maintained. We present SKiM (Serial KinderMiner), a generalized LBD system for processing any combination of A, Bs, and Cs.