AIMC Topic: Linkage Disequilibrium
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Performance of deep-learning-based approaches to improve polygenic scores.
Nature communications
Jun 2, 2025
Polygenic scores, which estimate an individual's genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonl...
Exploring new drug treatment targets for immune related bone diseases using a multi omics joint analysis strategy.
Scientific reports
Mar 27, 2025
In the field of treatment and prevention of immune-related bone diseases, significant challenges persist, necessitating the urgent exploration of new and effective treatment methods. However, most existing Mendelian randomization (MR) studies are con...
Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.
International journal of molecular sciences
Feb 27, 2025
Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases l...
Identification of Taihang-chicken-specific genetic markers using genome-wide SNPs and machine learning: BREED-SPECIFIC SNPS OF TAIHANG CHICKEN.
Poultry science
Nov 22, 2024
Taihang is an indigenous breed in Hebei Province and has a long history of evolution. To uncover the genetic basis and protect the genetic resources, it is important to develop accurate markers to identify Taihang at the molecular level. In this stud...
Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.
Journal of human genetics
Jan 15, 2024
The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation me...
A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes.
Nature communications
Mar 12, 2021
Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethn...
Evaluating the informativeness of deep learning annotations for human complex diseases.
Nature communications
Sep 17, 2020
Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learn...
Exploring gene-gene interaction in family-based data with an unsupervised machine learning method: EPISFA.
Genetic epidemiology
Sep 1, 2020
Gene-gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were oft...
Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
May 24, 2020
We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, buil...
Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.
Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
Feb 5, 2020
The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore c...
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