AIMC Topic: Phenotype

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ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.

Genetics, selection, evolution : GSE
BACKGROUND: Various methods have been widely utilized to estimate the genomic breeding values (GEBVs) for genomic prediction. Traditional approaches often relied on the assumption of linear regression models, which struggle to effectively capture the...

GermVersity: A free and user-friendly interface to enhance the visualization and analysis of genebank data.

PloS one
Genebanks are crucial for food security and industrial applications. However, their heterogeneous nature hinders effective utilization. To address this, the GermVersity platform was developed to integrate conventional, artificial intelligence, and da...

Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
This study presents a Bayesian neural networks framework with LASSO regularization and the GSMeSP interpretability tool, enabling accurate, uncertainty-aware, and biologically interpretable genomic prediction. Deep learning offers significant potenti...

Optimizing intervertebral disc cell metabolic phenotyping with machine learning and artificial neural networks.

Scientific reports
Biological phenotyping of cellular metabolism is essential for deciphering health and disease states. The Seahorse XF analyzer enables direct measurement of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), providing insight ...

Evaluation of Lens culinaris germplasm against ascochyta blight by applying phenotyping and defense genes attributes.

Scientific reports
Lentil Ascochyta blight (caused by Ascochyta lentis) is one of the most important limiting factors of lentil cultivation and production in most regions of the world. Introducing resistance sources against the pathogen is a suitable strategy to conque...

Smartwatch-Derived Digital Phenotypes Relate to Psychopathology Dimensions in Patients With Psychotic Spectrum Disorders: Longitudinal Observational Study.

JMIR mental health
BACKGROUND: Digital phenotyping refers to the objective measurement of human behavior via devices such as smartphones or watches and constitutes a promising advancement in personalized medicine. Digital phenotypes derived from heart rate, mobility, o...

Phenotype-driven leaf deep metabolomics framework depicts key metabolisms and metabolites associated with yield traits in rice.

Planta
This study links rice leaf metabolome to yield traits, identifying 13 key metabolites through computational metabolomics. These enable early prediction of high-yield varieties, enhancing screening strategies in crop breeding. Metabolites serve as dyn...

Unsupervised discovery of ischemic stroke phenotypes from multimodal MRI radiomics.

Biomedical physics & engineering express
This study presents a fully unsupervised and label-independent radiomic pipeline designed to group different types of ischemic stroke lesions using multimodal Magnetic Resonance Imaging (MRI) . The aim is to address lesion heterogeneity and the absen...

From root to result: Portable NIRS-based non-destructive prediction of cassava quality traits.

PloS one
Cassava (Manihot esculenta Crantz) is a staple food and a key industrial crop across tropical regions, but traditional phenotyping for critical quality traits like dry matter content (DMC) and starch content (StC) is a laborious and low-throughput pr...

PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images.

PloS one
Multiplexed methods for the detection of protein expression generate extremely data-rich images of intact tissue sections. These images are invaluable for the quantification and analysis of complex biology and biomarker development. However, their in...