AIMC Topic: Polymorphism, Single Nucleotide
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Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.
Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
May 2, 2024
This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS...
DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.
Biostatistics (Oxford, England)
Apr 15, 2024
Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regre...
Hematological Indices and Genetic Variants of Premature Ovarian Insufficiency: Machine Learning Approaches.
Cardiovascular & hematological disorders drug targets
Jan 1, 2024
BACKGROUND: Premature Ovarian Insufficiency (POI) is associated with infertility. Little is known about the potential circulating biomarkers that could be used to predict POI. We have investigated the possible association between white and red blood ...
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...
Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease.
Journal of Alzheimer's disease : JAD
Jan 1, 2024
BACKGROUND: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addit...
Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.
Current drug metabolism
Jan 1, 2024
AIMS: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of paracetamol for new indications in prete...
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.
Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.
Cancer genomics & proteomics
Dec 1, 2023
Rapid advancements in high-throughput biological techniques have facilitated the generation of high-dimensional omics datasets, which have provided a solid foundation for precision medicine and prognosis prediction. Nonetheless, the problem of missin...
Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks.
Molecular biology and evolution
Oct 4, 2023
Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs of individuals and of the age of genomic variants is key in several population genetic analyses. We developed a likelihood-free approach, called CoalNN, which use...
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