A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications

TL;DR

This paper introduces KinderMiner, a simple and easy-to-implement text mining method for ranking pairwise associations, demonstrated in biomedical applications like identifying transcription factors and drug repositioning, with promising results.

Contribution

The paper presents KinderMiner, a novel, minimal-data, easy-to-use text mining approach for ranking associations, applicable beyond biomedical fields.

Findings

KinderMiner outperforms existing algorithms in biomedical tasks.

The method is effective for identifying relevant transcription factors.

It successfully suggests potential drugs for repositioning.

Abstract

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.