AIMC Topic: Phenotype

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Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.

PloS one Aug 28, 2024
We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidops...
Models, Genetic Quantitative Trait, Heritable Neural Networks, Computer Linear Models Machine Learning Arabidopsis Genome, Plant Phenotype Quantitative Trait Loci Genomics
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What could be the role of genetic tests and machine learning of AXIN2 variant dominance in non-syndromic hypodontia? A case-control study in orthodontically treated patients.

Progress in orthodontics Aug 26, 2024
BACKGROUND: Hypodontia is the most prevalent dental anomaly in humans, and is primarily attributed to genetic factors. Although genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNP) associated with hypodontia, g...
MSX1 Transcription Factor Genetic Testing Axin Protein Anodontia PAX9 Transcription Factor Adolescent Child Polymorphism, Single Nucleotide Genotype Humans Machine Learning Male Phenotype Female Middle Aged Young Adult Case-Control Studies Adult Saudi Arabia
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A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans.

PLoS genetics Aug 26, 2024
Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rat...
Kinesins Ethyl Methanesulfonate Machine Learning Mutation Rate Animals Phenotype Caenorhabditis elegans Mutation Caenorhabditis elegans Proteins Whole Genome Sequencing Chromosome Mapping Mutagenesis
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Machine learning analysis of oxidative stress-related phenotypes for specific gene screening in ovarian cancer.

Environmental toxicology Aug 20, 2024
BACKGROUND: Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress-related prognostic signature to enhance the prognos...
Middle Aged Cell Line, Tumor Oxidative Stress Gene Expression Regulation, Neoplastic Phenotype Humans Ovarian Neoplasms Prognosis Machine Learning Genetic Testing Female Biomarkers, Tumor
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Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

ESC heart failure Aug 19, 2024
AIMS: Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac i...
Unsupervised Machine Learning Heart Failure Coronary Care Units Male Middle Aged Hospital Mortality Follow-Up Studies Intensive Care Units Female Aged Survival Rate Humans Phenotype Retrospective Studies
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MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.

European journal of medicinal chemistry Aug 16, 2024
Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in...
Humans Phenotype Drug Discovery Deep Learning Molecular Structure Malaria Antimalarials Parasitic Sensitivity Tests Plasmodium
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AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations.

EBioMedicine Aug 16, 2024
BACKGROUND: Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications, clinical risk scores are commonly employed, but their predictive...
Female Male Atrial Fibrillation Critical Care Humans Artificial Intelligence Machine Learning Algorithms Phenotype
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Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping.

ACS nano Aug 15, 2024
Spectral fingerprinting has emerged as a powerful tool that is adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coup...
Nanotubes, Carbon RAW 264.7 Cells Machine Learning Macrophages Phenotype Animals Spectroscopy, Near-Infrared Mice DNA
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Definition and Validation of Prognostic Phenotypes in Moderate Aortic Stenosis.

JACC. Cardiovascular imaging Aug 14, 2024
BACKGROUND: Adverse outcomes from moderate aortic stenosis (AS) may be caused by progression to severe AS or by the effects of comorbidities. In the absence of randomized trial evidence favoring aortic valve replacement (AVR) in patients with moderat...
Humans Phenotype Prognosis Female Male Echocardiography Middle Aged Risk Assessment Reproducibility of Results Risk Factors Aged Aged, 80 and over Heart Failure Aortic Valve Stenosis Aortic Valve Unsupervised Machine Learning Predictive Value of Tests Comorbidity Severity of Illness Index Time Factors Cluster Analysis
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Unraveling phenotypic heterogeneity in stanford type B aortic dissection patients through machine learning clustering analysis of cardiovascular CT imaging.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese Aug 10, 2024
OBJECTIVE: Aortic dissection remains a life-threatening condition necessitating accurate diagnosis and timely intervention. This study aimed to investigate phenotypic heterogeneity in patients with Stanford type B aortic dissection (TBAD) through mac...
Phenotype Retrospective Studies Tomography, X-Ray Computed Female Humans Machine Learning Imaging, Three-Dimensional Cluster Analysis Aortic Dissection Aortic Aneurysm, Thoracic Computed Tomography Angiography Aged Adult Male Middle Aged
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