AIMC Topic: Phenotype
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Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.
The plant genome
Mar 1, 2025
Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underly...
Stimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions.
Human brain mapping
Feb 15, 2025
While the use of naturalistic stimuli such as movie clips for understanding individual differences and brain-behaviour relationships attracts increasing interest, the influence of stimulus selection remains largely unclear. By using machine learning ...
Predicting Fitness-Related Traits Using Gene Expression and Machine Learning.
Genome biology and evolution
Feb 3, 2025
Evolution by natural selection occurs at its most basic through the change in frequencies of alleles; connecting those genomic targets to phenotypic selection is an important goal for evolutionary biology in the genomics era. The relative abundance o...
Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping.
Briefings in functional genomics
Jan 15, 2025
Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digit...
MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.
Journal of experimental botany
Jan 10, 2025
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we i...
Prediction of phenotypes by secretory biomarkers and machine learning in patients with chronic rhinosinusitis.
European review for medical and pharmacological sciences
Jan 1, 2025
OBJECTIVE: Chronic rhinosinusitis (CRS) has traditionally been classified phenotypically according to the presence (CRSwNP) or absence (CRSsNP) of nasal polyps. However, the phenotypic dichotomy does not represent the complexity of the disease. Curre...
Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2025
OBJECTIVE: This study aims to automate the prediction of Mini-Mental State Examination (MMSE) scores, a widely adopted standard for cognitive assessment in patients with Alzheimer's disease, using natural language processing (NLP) and machine learnin...
Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2025
One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bact...
Learning genotype-phenotype associations from gaps in multi-species sequence alignments.
Briefings in bioinformatics
Nov 22, 2024
Understanding the genetic basis of phenotypic variation is fundamental to biology. Here we introduce GAP, a novel machine learning framework for predicting binary phenotypes from gaps in multi-species sequence alignments. GAP employs a neural network...
Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.
Briefings in bioinformatics
Nov 22, 2024
This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencin...
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