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Genome-Wide Association Study

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Machine Learning to Advance Human Genome-Wide Association Studies.

Genes
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from large...

Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.

Nature genetics
Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplet...

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age.

The lancet. Healthy longevity
BACKGROUND: Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framew...

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.

British journal of clinical pharmacology
Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. However, interpreting the biological implications of these associations remains a challenge. This revie...

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Translational psychiatry
Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk f...

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.

Circulation. Genomic and precision medicine
BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well u...

Improving variant calling using population data and deep learning.

BMC bioinformatics
Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to ...

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.

Sensors (Basel, Switzerland)
Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This ...

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

PloS one
The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of...

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Mammalian genome : official journal of the International Mammalian Genome Society
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for ...