AIMC Topic: Phenotype
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Automated Single Cell Phenotyping of Time-of-Flight Secondary Ion Mass Spectrometry Tissue Images.
Journal of the American Society for Mass Spectrometry
Nov 19, 2024
Existing analytical techniques are being improved or applied in new ways to profile the tissue microenvironment (TME) to better understand the role of cells in disease research. Fully understanding the complex interactions between cells of many diffe...
Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia.
Neurosurgery
Nov 18, 2024
BACKGROUND AND OBJECTIVES: Chiari type-1 malformation (CM1) and syringomyelia (SM) are common related pediatric neurosurgical conditions with heterogeneous clinical and radiological presentations that offer challenges related to diagnosis and managem...
Machine Learning Identifies Clinically Distinct Phenotypes in Patients With Aortic Regurgitation.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Nov 18, 2024
BACKGROUND: Aortic regurgitation (AR) is a prevalent valve disease with a long latent period before symptoms appear. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation.
Artificial intelligence modeling of biomarker-based physiological age: Impact on phase 1 drug-metabolizing enzyme phenotypes.
CPT: pharmacometrics & systems pharmacology
Nov 14, 2024
Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning-based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition E...
Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant from whole genome sequencing and gene expression.
Antimicrobial agents and chemotherapy
Nov 14, 2024
Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susce...
Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.
Circulation. Cardiovascular quality and outcomes
Nov 14, 2024
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. ...
Analysis of behavioral flow resolves latent phenotypes.
Nature methods
Nov 12, 2024
The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple te...
Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.
International journal of chronic obstructive pulmonary disease
Nov 5, 2024
BACKGROUND AND PURPOSE: Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on scree...
Polymorphism, Single Nucleotide
Genetic Association Studies
Logistic Models
Pulmonary Disease, Chronic Obstructive
Phenotype
Predictive Value of Tests
Lung
Humans
Male
Female
Case-Control Studies
Machine Learning
Middle Aged
Risk Factors
ROC Curve
Algorithms
Aged
Risk Assessment
Genetic Predisposition to Disease
Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping.
Talanta
Nov 2, 2024
The disease progression and treatment options of leukemia between different subtypes vary considerably, emphasizing the importance of phenotyping. However, early typing of leukemia remains challenging due to the lack of highly sensitive and specific ...
Machine learning algorithms translate big data into predictive breeding accuracy.
Trends in plant science
Oct 26, 2024
Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability...
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