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Updated: Jun 24, 2026

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

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

Published on: June 13, 2025

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation

Xinyu He¹, Ying Qu¹

¹School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, China.

Journal of Biomedical Informatics

|June 22, 2026

Summary

This summary is machine-generated.

The new Adaptive Spatial Perception and Hierarchical Reasoning (ASP-HR) model enhances document-level biomedical relation extraction by dynamically weighting entities and using hierarchical reasoning. This approach improves accuracy in identifying complex relationships within scientific literature.

Keywords:

Adaptive spatial perception Document-level biomedical relationship extraction Graph attention networks Hierarchical reasoning

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Published on: February 23, 2019

Area of Science:

Biomedical informatics
Natural Language Processing
Machine Learning

Background:

Document-level biomedical relation extraction is vital for advancing disease diagnosis and drug discovery.
Current methods struggle with static entity representations, document structure, and lack of global consistency.
These limitations hinder the accurate mining of complex relationships from biomedical literature.

Purpose of the Study:

To introduce a novel model, Adaptive Spatial Perception and Hierarchical Reasoning (ASP-HR), for improved document-level biomedical relation extraction.
To address the limitations of existing methods by incorporating dynamic entity weighting and hierarchical reasoning.
To enhance the accuracy and robustness of extracting biomedical relationships from text.

Main Methods:

Developed a three-level progressive architecture featuring the Dynamic Distance-Aware Entity Interaction Enhancement (DDA-EIE) module.
Implemented the Multi-Relation Graph Attention Reasoning Network (MR-GARN) for deep semantic dependency mining.
Integrated the Dual-Stage Inference Enhancement (DSIE) module for robust prediction through bias correction.

Main Results:

ASP-HR achieved superior performance on the CDR dataset with an Overall-F1 of 88.9%, outperforming the state-of-the-art by 1.8%.
The model demonstrated strong results on the GDA dataset (Overall-F1: 84.9%) and BioRED dataset (Overall-F1: 61.8%).
ASP-HR significantly improved Intra-F1 and Inter-F1 scores, indicating enhanced ability to capture both within-sentence and cross-sentence relationships.

Conclusions:

The proposed ASP-HR model effectively addresses key challenges in document-level biomedical relation extraction.
Adaptive spatial perception and hierarchical reasoning mechanisms are crucial for enhancing model performance and robustness.
ASP-HR represents a significant advancement in the field, offering improved accuracy for biomedical knowledge discovery.