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. We evaluate SKiM via the rediscovery of discoveries by Don Swanson, who pioneered LBD. Using only literature from the 19th century up to a year before Swanson's discoveries, SKiM uncovers all five discoveries. We apply SKiM to repurposing drugs for 26 conditions of high prevalence. Manual analysis confirmed 65 discoveries useful for four diseases from Swanson's discoveries from one to 31 years prior to their first validation by clinical trials. SKiM predicts many new potential drug candidates representing prime targets for wet lab validation. SKiM can be applied to any biomedical inquiry sufficiently mentioned in the literature.

中文翻译：

---

SKiM-基于通用文献的发现系统，用于从PubMed中发现新颖的生物医学知识

基于文献的发现（LBD）通过将术语A经由B中间物链接到C来发现未发现的公共知识。现有的LBD系统仅限于处理某些A，B和C项，并且其中的许多项均未维护。我们介绍了SKiM（串行KinderMiner），这是一种通用的LBD系统，用于处理A，B和C的任意组合。我们通过开拓LBD的Don Swanson重新发现发现来评估SKiM。SKiM仅使用19世纪至斯旺森发现前一年的文献，就发现了全部五个发现。我们将SKiM应用于26种高患病率条件下的药物重用。手动分析从Swanson的发现中提取了65个发现，这些发现对从四到十三年的发现都是有用的。SKiM预测许多新的潜在候选药物代表了湿实验室验证的主要目标。SKiM可应用于文献中充分提及的任何生物医学研究。