Welcome to KinderMiner Suite
The KinderMiner Suite provides a suite of tools for researchers to extract insights from over 38 million abstracts indexed in PubMed. All tools in the KinderMiner suite are free to use by anyone, with just an email needed to register!
KinderMiner
KinderMiner (KM) filters and ranks B-terms by their strength of association with an A-term based on co-occurrence in PubMed abstracts. This allows researchers to prioritize research directions and understand direct connections between A and B terms.
Serial KinderMiner
Serial KinderMiner (SKiM), filters and ranks B-terms by their strength of association with an A-term in PubMed abstracts, and then subsequently filters and ranks C-terms by their association with those same B-terms. This allows researchers to discover hidden, indirect connections between A and C terms even when the literature sources linking A to B and B to C might be from different literature domains.
Hypotheses Evaluation
After running a KinderMiner or Serial KinderMiner query, you can have an advanced AI model (large language model) evaluate your research hypotheses. Select your KM A-B or SKiM A-B-C relationships of interest, submit your hypothesis, and receive literature-backed evaluations - helping you accelerate your research.
If you find SKiM useful, please cite:
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05539-y