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

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Published on: October 13, 2023

Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Lun Hu^1,2,3, Menglong Zhang^1,2,3, Pengwei Hu^1,2,3

¹The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China.

Briefings in Bioinformatics

|March 1, 2024

Summary

This summary is machine-generated.

This study introduces HGHDA, a novel dual-channel hypergraph convolutional network, to predict herb-disease associations (HDAs) by effectively modeling complex multi-component, multi-target mechanisms. The model demonstrates superior performance over existing methods in predicting HDAs.

Keywords:

Chinese traditional medicine herb–disease association prediction hypergraph convolutional network multi-target multi-component network pharmacology

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High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

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Published on: May 21, 2018

Area of Science:

Computational biology
Pharmacology
Bioinformatics

Background:

Traditional herbal medicine relies on thousands of years of experience for disease treatment.
Understanding herb-disease associations (HDAs) is challenging due to the complex multi-target, multi-component (MTMC) nature of botanical therapeutics.
Existing prediction models often fail to capture the intricate MTMC mechanisms inherent in herbal medicine.

Purpose of the Study:

To propose a novel computational model, HGHDA, for predicting herb-disease associations (HDAs).
To address the limitations of current models by incorporating the multi-target, multi-component (MTMC) mechanism of herbal therapeutics.
To enhance the accuracy and reliability of predicting potential therapeutic uses of herbs.

Main Methods:

Developed a dual-channel hypergraph convolutional network (HGHDA) for HDA prediction.
Utilized an autoencoder to generate low-dimensional embeddings for herb components and target proteins.
Employed hypergraph convolution in two channels to model high-order relationships between herbs-components and diseases-target proteins.
Aggregated embeddings through the dual-channel network for prediction using a scoring function.

Main Results:

HGHDA demonstrated superior performance compared to state-of-the-art algorithms on two benchmark datasets.
Extensive experiments validated the model's effectiveness in predicting herb-disease associations.
Case studies on Chuan Xiong and Astragalus membranaceus showed high accuracy, with 7 and 8 out of top 10 predicted diseases confirmed in literature.

Conclusions:

The proposed HGHDA model effectively predicts herb-disease associations by capturing complex MTMC mechanisms.
HGHDA offers a significant advancement over existing methods for computational drug discovery and herbal medicine research.
The model's predictive accuracy and validated case studies highlight its potential for identifying novel therapeutic applications of herbs.