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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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BioCause: Annotating and analysing causality in the biomedical domain.

Claudiu Mihăilă¹, Tomoko Ohta, Sampo Pyysalo

¹The National Centre for Text Mining, School of Computer Science, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. claudiu.mihaila@cs.man.ac.uk

BMC Bioinformatics

|January 18, 2013

Summary

We developed BioCause, a new dataset of biomedical literature annotated for causality. This resource aids in developing automatic systems to recognize causal relationships, crucial for advancing biomedical knowledge discovery and hypothesis generation.

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Area of Science:

Biomedical Informatics
Natural Language Processing
Text Mining

Background:

Biomedical corpora with event information are vital for information extraction (IE).
Existing annotations often lack comprehensive coverage of all causal associations.
Causality is fundamental to biomedical knowledge, including diagnosis and systems biology.

Purpose of the Study:

To create a comprehensive resource for identifying all causal associations in biomedical discourse.
To develop and evaluate biomedical text mining tools for causality recognition.

Main Methods:

Defined an annotation scheme for causality relations in biomedical texts.
Annotated 851 causal relations across 19 infectious disease articles to create the BioCause dataset.
Achieved inter-annotator agreement over 60% for triggers and 80% for arguments.

Main Results:

The BioCause dataset comprises 19 open-access articles with pre-annotated named entities and events.
Analysis of causality relations within BioCause provides insights for system development.
Agreement rates improved significantly with a relaxed match setting.

Conclusions:

Augmenting existing annotations with causal relations enhances IE systems.
This resource supports tasks like textual inference, fact discovery, and hypothesis generation.
Improved IE systems can accelerate experimental work and biomedical knowledge discovery.