AIMC Topic: Phenotype
Clear Filters Showing 111 to 120 of 918 articles
Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.
Journal of behavioral medicine
Sep 21, 2024
Physical inactivity is a significant public health concern. Consideration of inter-individual variations in physical activity (PA) trends can provide additional information about the groups under study to aid intervention design. This study aims to i...
Methods for phenotyping adult patients with acute kidney injury: a systematic review.
Journal of nephrology
Sep 16, 2024
BACKGROUND: Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to cust...
Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.
Nature genetics
Sep 11, 2024
The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, ...
Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large-scale soybean dataset.
The plant genome
Sep 10, 2024
The surge in high-throughput technologies has empowered the acquisition of vast genomic datasets, prompting the search for genetic markers and biomarkers relevant to complex traits. However, grappling with the inherent complexities of high dimensiona...
Machine learning phenotyping and GWAS reveal genetic basis of Cd tolerance and absorption in jute.
Environmental pollution (Barking, Essex : 1987)
Sep 10, 2024
Cadmium (Cd) is a dangerous environmental contaminant. Jute (Corchorus sp.) is an important natural fiber crop with strong absorption and excellent adaptability to metal-stressed environments, used in the phytoextraction of heavy metals. Understandin...
Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Sep 6, 2024
Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised ma...
Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.
European journal of heart failure
Sep 6, 2024
AIMS: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abn...
Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.
The Journal of investigative dermatology
Sep 3, 2024
Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally nonquantitative or are low throughput owing to technical limitations of splitting hairs. We developed a deep learnin...
Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.
BMJ open
Aug 30, 2024
INTRODUCTION: Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with p...
Radiomics-based machine learning model to phenotype hip involvement in ankylosing spondylitis: a pilot study.
Frontiers in immunology
Aug 29, 2024
OBJECTIVES: Hip involvement is an important reason of disability in patients with ankylosing spondylitis (AS). Unveiling the potential phenotype of hip involvement in AS remains an unmet need to understand its biological mechanisms and improve clinic...
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