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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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MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction.

Wenhao Gu^1,2, Xiao Yang², Minhao Yang¹

¹College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.

Bioinformatics Advances

|January 26, 2023

Summary

This summary is machine-generated.

MarkerGenie enhances biomedical marker discovery by improving natural language processing (NLP) for extracting relations between biological entities from text. This tool addresses challenges in biomedical NLP, offering more accurate and efficient literature analysis.

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Area of Science:

Computational Biology
Bioinformatics
Natural Language Processing

Background:

Natural Language Processing (NLP) has advanced significantly, yet its application in the biomedical domain is hindered by data scarcity and terminology issues.
Accurate extraction of biomedical entity relations is crucial for marker discovery, but manual curation is time-consuming and prone to bias.
Automated NLP tools can accelerate literature surveys and reduce bias in biomedical research.

Purpose of the Study:

To develop an improved NLP program, MarkerGenie, for biomedical entity relation extraction.
To address the challenges of limited labeled data and complex biological terminology in biomedical NLP.
To enhance the accuracy and efficiency of identifying associations between biomedical entities.

Main Methods:

Implemented automatic training data labeling for improved model performance.
Incorporated rule-based biological terminology cleaning to handle ambiguities.
Developed a more accurate NLP model for predicting binary associative and multi-relations.

Main Results:

The MarkerGenie program demonstrated effectiveness in identifying relations between biomedical entities.
Validation was performed on various benchmark datasets and through case studies.
The proposed methods showed improved accuracy in relation prediction.

Conclusions:

MarkerGenie offers a valuable tool for automated biomedical entity relation extraction.
The program's advancements in NLP address key challenges in the biomedical domain.
MarkerGenie facilitates more thorough and less biased literature surveys for marker discovery.