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

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PredinID: Predicting Pathogenic Inframe Indels in Human Through Graph Convolution Neural Network With Graph Sampling

Zhenyu Yue, Ying Xiang, Guojun Chen

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|April 11, 2023

Summary

This summary is machine-generated.

Predicting pathogenic inframe insertion/deletion (indel) variants is crucial for disease research. PredinID, a novel graph convolutional network method, accurately identifies disease-associated indels, outperforming existing computational approaches.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Inframe insertion/deletion (indel) variants can significantly alter protein function and are linked to various diseases.
Accurate in silico modeling and pathogenicity prediction of indels are challenging due to limited experimental data and computational tools.

Purpose of the Study:

To develop a novel computational method, PredinID (Predictor for inframe InDels), for predicting pathogenic inframe indels.
To leverage graph convolutional networks (GCNs) and k-nearest neighbor (KNN) algorithms for improved indel pathogenicity prediction.

Main Methods:

Constructed a feature graph using the KNN algorithm to aggregate informative representations for indel prediction.
Framed pathogenic inframe indel prediction as a node classification task within a GCN framework.
Employed an edge-based sampling strategy to capture feature space connections and subgraph topological structures.

Main Results:

PredinID demonstrated satisfactory performance, validated through 5-fold cross-validations.
The method outperformed four classic machine learning algorithms and two existing GCN methods in predictive accuracy.
Experimental results on an independent test set confirmed PredinID's superior performance compared to state-of-the-art methods.

Conclusions:

PredinID offers a robust and accurate computational approach for predicting pathogenic inframe indels.
The developed GCN-based method enhances the understanding of indel pathogenicity in disease contexts.
A web server is available to facilitate the practical application of PredinID in research.