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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models.

Robert J Millikin¹, Kalpana Raja^1,2, John Steill¹

¹Morgridge Institute for Research, Madison, WI, USA.

BMC Bioinformatics

|November 2, 2023

Summary

Serial KinderMiner (SKiM) is a new literature-based discovery tool that helps researchers find hidden connections between biomedical concepts. This open-source algorithm and web interface identify relationship types and support large-scale queries for drug repurposing and disease research.

Keywords:

Biomedical text mining Knowledge graph Literature-based discovery Relation extraction

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Area of Science:

Biomedical Informatics
Computational Biology
Data Mining

Background:

The exponential growth of biomedical literature necessitates efficient tools for knowledge discovery.
Literature-based discovery (LBD) aims to uncover novel associations between concepts in disparate research domains.
Existing LBD tools often lack flexibility, interpretability, or scalability for complex biomedical research queries.

Purpose of the Study:

To introduce Serial KinderMiner (SKiM), an open-source LBD algorithm and web interface.
To address limitations of current LBD tools, including relationship type identification, user-defined term lists, and scalability for large concept sets.
To facilitate the discovery of A-B-C relationships between biomedical concepts.

Main Methods:

Developed the Serial KinderMiner (SKiM) algorithm for identifying statistically significant A-B-C linkages.
Integrated SKiM with a knowledge graph utilizing transformer machine-learning models for relationship interpretation.
Created an open-source, user-friendly web interface with comprehensive biomedical concept lists.

Main Results:

Demonstrated SKiM's efficacy in discovering A-B-C linkages through experiments in classic LBD, drug repurposing, and cancer-related research.
Successfully supplemented SKiM with a knowledge graph to provide relationship type labels.
Launched an accessible web interface (skim.morgridge.org) for performing SKiM searches.

Conclusions:

SKiM is a versatile, domain-general algorithm for LBD searches.
The tool enables discovery of relationships between user-defined concepts, supporting thousands of C terms.
SKiM enhances LBD by identifying relationship types, moving beyond simple association detection.