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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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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Related Experiment Video

Updated: Sep 20, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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Combining K Nearest Neighbor With Nonnegative Matrix Factorization for Predicting Circrna-Disease Associations.

Mei-Neng Wang, Xue-Jun Xie, Zhu-Hong You

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|June 8, 2022

Summary

This summary is machine-generated.

This study introduces KNN-NMF, a computational method to identify links between circular RNAs (circRNAs) and diseases. KNN-NMF effectively predicts these associations, aiding disease understanding and diagnosis.

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Last Updated: Sep 20, 2025

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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:

Genomics
Bioinformatics
Computational Biology

Background:

Circular RNAs (circRNAs) are crucial regulators of gene expression.
circRNAs are implicated in various complex human diseases.
Experimental identification of circRNA-disease associations is resource-intensive.

Purpose of the Study:

To develop a computational model for inferring circRNA-disease associations.
To address the need for efficient and cost-effective methods in this field.

Main Methods:

Proposed a novel method, KNN-NMF, combining K-nearest neighbors (KNN) and Nonnegative Matrix Factorization (NMF).
Calculated Gaussian Interaction Profile (GIP) kernel similarity for circRNAs and diseases.
Incorporated disease semantic similarity.
Constructed new circRNA-disease interaction profiles using weighted KNN to mitigate false negatives.

Main Results:

KNN-NMF demonstrated superior prediction performance compared to existing methods in five-fold cross-validation.
Case studies confirmed KNN-NMF's efficacy in identifying potential circRNA-disease associations for common diseases.

Conclusions:

KNN-NMF is an effective computational tool for predicting circRNA-disease associations.
The method aids in understanding disease pathogenesis and facilitates diagnostic strategies.