AIMC Topic: Genotype

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Expanding biobank pharmacogenomics through machine learning calls of structural variation.

Genetics
Biobanks linking genetic data with clinical health records provide exciting opportunities for pharmacogenomic (PGx) research on genetic variation and drug response. Designed as central and multiuse resources, biobanks can facilitate diverse PGx resea...

Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).

The plant genome
For important food crops such as the common bean (Phaseolus vulgaris, L.), global demand continues to outpace the rate of genetic gain for quantitative traits. In this study, we leveraged the multi-environment trial (MET) dataset from the cooperative...

Genomic selection: Essence, applications, and prospects.

The plant genome
Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype-phenotype relationship...

Analysis of protein determinants of genotype-specific properties of group a rotaviruses using machine learning.

Computers in biology and medicine
Group A rotaviruses (RVAs) are the leading cause of viral diarrhoea across various host species, including mammals and birds. The VP7 and VP4 proteins of these viruses play critical roles in determining genotype specificity, influencing viral infecti...

Tasselyzer, a machine learning method to quantify maize anther exertion, based on PlantCV.

The Plant journal : for cell and molecular biology
Maize anthers emerge from male-only florets, a process that involves complex genetic programming and is affected by environmental factors. Quantifying anther exertion provides a key indicator of male fertility; however, traditional manual scoring met...

Hepatitis C Virus Saint Petersburg Variant Detection With Machine Learning Methods.

Journal of medical virology
Hepatitis C virus infection is a significant global health concern, affecting millions worldwide. Although direct-acting antivirals achieve over 90% success rate, treatment failures still occur, particularly when pan-genotypic DAAs are unavailable, a...

Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer's dementia.

Cell reports methods
High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). However, obtaining an accurate HDL size measurement is challenging. We demonstrated the utility of m...

Noninvasive fetal genotyping using deep neural networks.

Briefings in bioinformatics
Circulating cell-free DNA (cfDNA) is a powerful diagnostics tool that is widely studied in the context of liquid biopsy in oncology and other fields. In obstetrics, maternal plasma cfDNA have already proven its utility, enabling noninvasive prenatal ...

Learning genotype-phenotype associations from gaps in multi-species sequence alignments.

Briefings in bioinformatics
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...

Machine learning to optimize automated RH genotyping using whole-exome sequencing data.

Blood advances
Rh phenotype matching reduces but does not eliminate alloimmunization in patients with sickle cell disease (SCD) due to RH genetic diversity that is not distinguishable by serological typing. RH genotype matching can potentially mitigate Rh alloimmun...