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Related Experiment Video

Updated: May 17, 2025

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

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

Published on: October 13, 2023

Robust temporal knowledge inference via pathway snapshots with liquid neural network.

Peifu Han¹, Jianmin Wang², Dayan Liu¹

¹College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.

Methods (San Diego, Calif.)

|May 11, 2025

Summary

This summary is machine-generated.

This study introduces temporal knowledge inference agents using liquid neural networks to model dynamic biological data. These agents demonstrate robust decision-making and skill transfer in complex, changing environments.

Keywords:

Disease Pathways Liquid Neural Networks Relation Reasoning Robust Decision-Making Temporal Knowledge Inference

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Area of Science:

Computational Biology
Artificial Intelligence
Neuroscience

Background:

Static graphs are common for biological data but fail with dynamic processes like disease progression.
Real-world biological systems exhibit complex, evolving relationships challenging traditional methods.

Purpose of the Study:

To develop temporal knowledge inference agents for disease pathways.
To enable effective relation reasoning beyond training environments under complex shifts.

Main Methods:

An imitation learning framework utilizing liquid neural networks (LNNs).
LNNs are continuous-time neural models inspired by brain function, offering causality and adaptability.
The framework distills tasks from knowledge graphs, accounting for temporal evolution.

Main Results:

Liquid agents successfully transferred temporal skills to novel time nodes.
Demonstrated robustness in decision-making under complex environmental shifts.
Outperformed state-of-the-art deep reinforcement learning agents in temporal robustness.

Conclusions:

Liquid neural networks offer unique temporal robustness for decision-making in dynamic systems.
The proposed agents are effective for modeling and reasoning about temporal biological pathways.
This approach advances the analysis of dynamic biological and biomedical data.