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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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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Network-Assisted Disease Classification and Biomarker Discovery.

Sonja Strunz¹, Olaf Wolkenhauer^2,3, Alberto de la Fuente⁴

¹Biomathematics and Bioinformatics Unit, Leibniz-Institute for Farm Animal Biology (FBN), Institute of Genetics and Biometry, Wilhelm-Stahl-Allee 2, Dummerstorf, 18196, Germany.

Methods in Molecular Biology (Clifton, N.J.)

|December 18, 2015

Summary

Biomarker discovery from omics data faces reproducibility challenges. Network-assisted methods improve biomarker signatures for better disease diagnosis and treatment.

Keywords:

Biomarker discovery Classification Feature selection Molecular networks Pathways

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Area of Science:

Bioinformatics
Systems Medicine
Biomedical Research

Background:

High-throughput omics data are crucial for biomarker discovery in disease research.
Current biomarker signatures often lack reproducibility and biological interpretability, hindering clinical application.
Incorporating prior knowledge from pathways and molecular networks is a promising strategy.

Purpose of the Study:

To address the challenges of reproducibility and interpretability in biomarker discovery.
To review network-assisted approaches for identifying robust biomarker signatures.
To facilitate the clinical application of biomarkers identified from omics data.

Main Methods:

Review of two network-assisted approaches for biomarker discovery.
Approach 1: Inferring pathway activity scores for classification.
Approach 2: Differential network analysis to identify molecular subnetworks as biomarkers.

Main Results:

Network-assisted methods enhance the reproducibility and biological interpretability of biomarker signatures.
Pathway activity scores can be effectively used for disease classification.
Identified molecular subnetworks serve as promising biomarker signatures.

Conclusions:

Network integration significantly improves biomarker discovery from omics data.
The reviewed methods offer strategies to overcome current limitations in biomarker research.
Enhanced biomarker signatures hold potential for improved disease diagnosis, treatment, and prevention.