A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications
This provides a practical tool for biomedical researchers to efficiently propose ranked associations, though it appears incremental as it builds on existing text mining approaches.
The authors tackled the problem of ranking pairwise associations in biomedical applications by developing KinderMiner, a simple text mining method, and applied it to identify transcription factors for cell reprogramming and potential drugs for drug repositioning, showing compelling results compared to existing data and state-of-the-art algorithms.
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.