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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Updated: Sep 19, 2025

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

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Higher-Order Weighted Perturbation-Based Multilevel Information Fusion Model for Predicting CircRNA-Disease

Shanchen Pang^1,2,3, Zheqi Song¹, Yunyin Li¹

¹College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), Qingdao 266580, China.

Journal of Chemical Information and Modeling

|June 17, 2025

Summary

This summary is machine-generated.

This study introduces a new model for predicting disease-associated circular RNAs (circRNAs). The high-order weighted perturbation-based multilevel information fusion model (HWP-MIFM) effectively captures complex relationships for improved disease mechanism understanding.

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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Circular RNAs (circRNAs) play a role in disease initiation and progression.
Current computational methods often miss higher-order circRNA-disease associations and multilevel features.

Purpose of the Study:

To develop a novel computational model for predicting circRNA-disease associations.
To address limitations in existing methods by capturing higher-order and multilevel features.

Main Methods:

Proposed the high-order weighted perturbation-based multilevel information fusion model (HWP-MIFM).
Employed higher-order weighted perturbation for dynamic weight adjustment and higher-order association extraction.
Utilized dual-stage matrix factorization for multilayer structure construction and linear feature extraction.
Incorporated a dual-path feature learning module to capture complex nonlinear relationships.

Main Results:

HWP-MIFM demonstrated superior overall performance compared to seven state-of-the-art methods in 5-fold cross-validation across four datasets.
Ablation studies and case analyses validated the model's accuracy and practical utility.

Conclusions:

The HWP-MIFM model offers a more comprehensive approach to predicting circRNA-disease associations.
This advancement aids in understanding disease mechanisms and identifying potential therapeutic targets.