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

arXiv: https://arxiv.org/abs/1906.05255 · HTML: https://mlsanity.com/paper/1906.05255/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Accelerating COVID-19 research with graph mining and transformer-based learning](https://mlsanity.com/paper/2102.07631/) (2021)

## Abstract (arXiv)

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.