  We present a simple text mining method that is easy to implement, requires
minimal data collection and preparation, and is easy to use for proposing
ranked associations between a list of target terms and a key phrase. We call
this method KinderMiner, and apply it to two biomedical applications. The first
application is to identify relevant transcription factors for cell
reprogramming, and the second is to identify potential drugs for investigation
in drug repositioning. We compare the results from our algorithm to existing
data and state-of-the-art algorithms, demonstrating compelling results for both
application areas. While we apply the algorithm here for biomedical
applications, we argue that the method is generalizable to any available corpus
of sufficient size.
