AIMC Topic: Genotype
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Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models.
Scientific reports
Feb 5, 2025
Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different...
Multimodal learning for mapping genotype-phenotype dynamics.
Nature computational science
Jan 28, 2025
How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers...
Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.
Translational psychiatry
Jan 21, 2025
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was s...
Association of and gene polymorphisms and ERAP2 protein with the susceptibility and severity of rheumatoid arthritis in the Ukrainian population.
Frontiers in immunology
Jan 21, 2025
INTRODUCTION: Rheumatoid arthritis (RA) is a long-term autoimmune disorder that primarily affects joints. Although RA is chiefly associated with HLA class II, nevertheless some HLA class I associations have also been observed. These molecules present...
Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.
Methods (San Diego, Calif.)
Jan 19, 2025
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations betwe...
Multiple, Single Trait GWAS and Supervised Machine Learning Reveal the Genetic Architecture of Fraxinus excelsior Tolerance to Ash Dieback in Europe.
Plant, cell & environment
Jan 17, 2025
Common ash (Fraxinus excelsior) is under intensive attack from the invasive alien pathogenic fungus Hymenoscyphus fraxineus, causing ash dieback at epidemic levels throughout Europe. Previous studies have found significant genetic variation among gen...
Genome-wide association study on color-image-based convolutional neural networks.
PeerJ
Jan 13, 2025
BACKGROUND: Convolutional neural networks have excellent modeling abilities to complex large-scale datasets and have been applied to genomics. It requires converting genotype data to image format when employing convolutional neural networks to genome...
Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in Mannheimia haemolytica.
Veterinary microbiology
Jan 8, 2025
Mannheimia haemolytica is one of the most common causative agents of bovine respiratory disease (BRD); however, antibiotic resistance in this species is increasing, making treatment more difficult. Integrative-conjugative elements (ICE), a subset of ...
Single-nucleotide polymorphisms in genes associated with the vitamin D pathway related to clinical and therapeutic outcomes of American tegumentary leishmaniasis.
Frontiers in cellular and infection microbiology
Jan 8, 2025
BACKGROUND: The vitamin D pathway contributes to the microbicidal activity of macrophages against infection. In addition to induction of this pathway, interferon-gamma (IFNγ), interleukin (IL)-15, and IL32γ are part of a network of pro-inflammatory ...
Mind the Gap: A Neural Network Framework for Imputing Genotypes in Non-Model Species.
Molecular ecology resources
Jan 3, 2025
Reduced representation sequencing (RRS) has proven to be a cost-effective solution for sequencing subsets of the genome in non-model species for large-scale studies. However, the targeted nature of RRS approaches commonly introduces large amounts of ...
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