AIMC Topic: Genome-Wide Association Study
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scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data.
GigaScience
Jan 2, 2024
BACKGROUND: Exploring the cellular processes of genes from the aspects of biological networks is of great interest to understanding the properties of complex diseases and biological systems. Biological networks, such as protein-protein interaction ne...
Deep Learning-Based HLA Allele Imputation Applicable to GWAS.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Human leukocyte antigen (HLA) imputation is an essential step following genome-wide association study, particularly when putative associations in HLA genes are identified, to fully understand the genetic basis of human traits. Different HLA imputatio...
Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.
Journal of Alzheimer's disease : JAD
Jan 1, 2024
BACKGROUND: Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail t...
Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.
Journal of Alzheimer's disease : JAD
Jan 1, 2024
BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming...
Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches.
Combinatorial chemistry & high throughput screening
Jan 1, 2024
INTRODUCTION: To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches.
Predicting functional consequences of SNPs on mRNA translation via machine learning.
Nucleic acids research
Aug 25, 2023
The functional impact of single nucleotide polymorphisms (SNPs) on translation has yet to be considered when prioritizing disease-causing SNPs from genome-wide association studies (GWAS). Here we apply machine learning models to genome-wide ribosome ...
A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants.
Briefings in bioinformatics
Jan 19, 2023
Quantifying an individual's risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk ...
The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.
Nucleic acids research
Jan 6, 2023
The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industr...
How data science and AI-based technologies impact genomics.
Singapore medical journal
Jan 1, 2023
Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and pheno...
DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.
Bioinformatics (Oxford, England)
Dec 13, 2022
MOTIVATION: Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited...
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