AIMC Topic: Genotype

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Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Sensors (Basel, Switzerland)
Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and tr...

Biological Prior Knowledge-Embedded Deep Neural Network for Plant Genomic Prediction.

Genes
Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed mode...

Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group A .

Journal of microbiology and biotechnology
Group A (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these finding...

Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.

Nature communications
New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-po...

SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants.

Nature communications
Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly occurring at repetitive genomic regions, from short-read sequencing data remains challenging. Here,...

Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framewo...

Profiling the gut microbiota to assess infection risk in -colonized patients.

Gut microbes
Vornhagen et al. introduced a model combining gut microbiota structure and genotype to assess infection risk in -colonized patients. Building on their findings, we investigated the gut microbiota composition and genotype in 16 colonized patients, f...

Comparing statistical learning methods for complex trait prediction from gene expression.

PloS one
Accurate prediction of complex traits is an important task in quantitative genetics. Genotypes have been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regression methods, dimension reduction me...

Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models.

Scientific reports
Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different...