AIMC Topic: Genetic Variation

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DANN: a deep learning approach for annotating the pathogenicity of genetic variants.

Bioinformatics (Oxford, England) Oct 22, 2014
UNLABELLED: Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-c...
Genome, Human Humans Molecular Sequence Annotation Algorithms Area Under Curve Selection, Genetic Support Vector Machine Genetic Variation Computer Graphics Neural Networks, Computer
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f-statistics-based ancestry profiling and convolutional neural network phenotyping shed new light on the structure of genetic and spike shape diversity in Aegilops tauschii Coss.

Proceedings of the Japan Academy. Series B, Physical and biological sciences Jun 11, 2025
Aegilops tauschii Coss., a progenitor of bread wheat, is an important wild genetic resource for breeding. The species comprises three genetically defined lineages (TauL1, TauL2, and TauL3), each displaying valuable phenotypes in agronomic traits, inc...
Convolutional Neural Networks Genetic Variation Aegilops Neural Networks, Computer Phenotype
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Digenic variant interpretation with hypothesis-driven explainable AI.

NAR genomics and bioinformatics Jun 1, 2025
The digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations of variants based on the proband's phenotypes and f...
Machine Learning Phenotype Computational Biology Humans Artificial Intelligence Genetic Variation Rare Diseases
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Global intraspecific diversity of marine forests of brown macroalgae predicted by past climate conditions.

Communications biology May 10, 2025
Global patterns of intraspecific genetic diversity are key to understanding evolutionary and ecological processes. However, insights into the distribution and drivers of genetic diversity remain limited, particularly for marine species. Here, we expl...
Seaweed Climate Phaeophyceae Biodiversity Genetic Variation
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The Role of Artificial Intelligence in Identifying Gene Variants and Improving Diagnosis.

Genes May 7, 2025
Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder caused by mutations in the gene, typically diagnosed during early childhood and characterized by significant phenotypic heterogeneity. Despite advancements in next-generation sequencin...
High-Throughput Nucleotide Sequencing Humans Neurofibromin 1 Artificial Intelligence Neurofibromatosis 1 Mutation Genetic Variation
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MIRACN: a residual convolutional neural network for predicting cell line specific functional regulatory variants.

Briefings in bioinformatics Mar 4, 2025
In post-genome-wide association study era, interpretation of noncoding variants remains a significant challenge due to their complexity and the limited understanding of their functions. Here, we developed MIRACN, a novel residual convolutional neural...
Cell Line Genetic Variation Humans Neural Networks, Computer Genome-Wide Association Study Computational Biology Convolutional Neural Networks
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A graph neural network approach for accurate prediction of pathogenicity in multi-type variants.

Briefings in bioinformatics Mar 4, 2025
Accurate prediction of pathogenic variants in human disease-associated genes would have a profound effect on clinical decision-making; however, it remains a significant challenge due to the overwhelming number of these variants. We propose graph neur...
Humans Neural Networks, Computer Databases, Genetic Genetic Variation Graph Neural Networks Deep Learning Computational Biology
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Hepatitis C Virus Saint Petersburg Variant Detection With Machine Learning Methods.

Journal of medical virology Feb 1, 2025
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...
Russia Machine Learning Molecular Epidemiology Genotype Genotyping Techniques Viral Nonstructural Proteins Hepatitis C Genetic Variation Hepacivirus Humans
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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...
Humans Machine Learning Genome-Wide Association Study Bacteria Phenotype Genetic Variation Genome, Bacterial Food Microbiology Foodborne Diseases
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LOGOWheat: deep learning-based prediction of regulatory effects for noncoding variants in wheats.

Briefings in bioinformatics Nov 22, 2024
Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in wheat has provided an opportunity to model the functional impacts of these variants. In this study, ...
Epigenomics Triticum Software Deep Learning Computational Biology Genetic Variation Genome, Plant
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