AIMC Topic: Alleles

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Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis.

Genome research May 2, 2025
Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F crosses with extensive genetic diversity...
Deep Learning Protein Binding Transcription Factors Animals Drosophila Proteins Drosophila melanogaster Binding Sites Drosophila Alleles Genetic Variation Embryonic Development
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SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants.

Nature communications Mar 11, 2025
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,...
Sheep Genome, Human Genotyping Techniques Genomics Genotype Alleles Genomic Structural Variation Animals Cattle Humans Machine Learning
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Machine learning enhanced immunologic risk assessments for solid organ transplantation.

Scientific reports Mar 7, 2025
The purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML). Prediction of crossmatch compatibility is complex and requires an understanding of the recipient an...
Risk Assessment ROC Curve Tissue Donors Organ Transplantation Humans Machine Learning HLA Antigens Alleles Histocompatibility Testing
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A machine learning approach to predict treatment efficacy and adverse effects in major depression using CYP2C19 and clinical-environmental predictors.

Psychiatric genetics Mar 5, 2025
BACKGROUND: Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Polymorphisms in cytochrome P450 2C19 (CYP2C19) play a role in response and side effects to medication...
Antidepressive Agents Cytochrome P-450 CYP2C19 Humans Treatment Outcome Adult Middle Aged Alleles Cross-Sectional Studies Machine Learning Depressive Disorder, Major Male Female
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LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks.

Frontiers in immunology Nov 28, 2024
INTRODUCTION: The identification of peptides eluted from HLA complexes by mass spectrometry (MS) can provide critical data for deep learning models of antigen presentation prediction and promote neoantigen vaccine design. A major challenge remains in...
Neural Networks, Computer Software Peptides Antigens, Neoplasm Mass Spectrometry Deep Learning Computational Biology Humans HLA Antigens Alleles Antigen Presentation
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Machine learning-based classification reveals distinct clusters of non-coding genomic allelic variations associated with Erm-mediated antibiotic resistance.

mSystems Jul 2, 2024
UNLABELLED: The erythromycin resistance RNA methyltransferase () confers cross-resistance to all therapeutically important macrolides, lincosamides, and streptogramins (MLS phenotype). The expression of is often induced by the macrolide-mediated rib...
Methyltransferases Nucleic Acid Conformation Erythromycin Bacterial Proteins Machine Learning Alleles Anti-Bacterial Agents Genetic Variation Drug Resistance, Bacterial
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Fundamentals for predicting transcriptional regulations from DNA sequence patterns.

Journal of human genetics May 10, 2024
Cell-type-specific regulatory elements, cataloged through extensive experiments and bioinformatics in large-scale consortiums, have enabled enrichment analyses of genetic associations that primarily utilize positional information of the regulatory el...
Computational Biology Sequence Analysis, DNA Alleles Transcription, Genetic Regulatory Sequences, Nucleic Acid Gene Expression Regulation Humans Base Sequence Machine Learning Genomics
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Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.

Frontiers in immunology Jan 16, 2024
Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, investigating the composition of training datasets used in machine learning models with healthcare ap...
Alleles Humans Histocompatibility Antigens Class I Peptides Genes, MHC Class I Protein Binding
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Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

Genome biology Aug 4, 2023
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and m...
RNA, Messenger RNA Splice Sites Consensus Sequence RNA-Binding Proteins Alleles Mutation Base Sequence Bias Humans RNA Nucleotides Binding Sites Internet Computer Simulation RNA, Viral Datasets as Topic Nucleotide Motifs Deep Learning
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Detecting SNP markers discriminating horse breeds by deep learning.

Scientific reports Jul 18, 2023
The assignment of an individual to the true population of origin using a low-panel of discriminant SNP markers is one of the most important applications of genomic data for practical use. The aim of this study was to evaluate the potential of differe...
Animals Horses Deep Learning Genotype Plant Breeding Polymorphism, Single Nucleotide Alleles
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