AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Alleles

Showing 1 to 10 of 66 articles

Clear Filters

Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.

Frontiers in immunology
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...

LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks.

Frontiers in immunology
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...

Deep Learning-Based HLA Allele Imputation Applicable to GWAS.

Methods in molecular biology (Clifton, N.J.)
Human leukocyte antigen (HLA) imputation is an essential step following genome-wide association study, particularly when putative associations in HLA genes are identified, to fully understand the genetic basis of human traits. Different HLA imputatio...

Fundamentals for predicting transcriptional regulations from DNA sequence patterns.

Journal of human genetics
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...

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...

Machine learning-based classification reveals distinct clusters of non-coding genomic allelic variations associated with Erm-mediated antibiotic resistance.

mSystems
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...

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,...

Machine learning enhanced immunologic risk assessments for solid organ transplantation.

Scientific reports
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...

A machine learning approach to predict treatment efficacy and adverse effects in major depression using CYP2C19 and clinical-environmental predictors.

Psychiatric genetics
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...

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

Genome research
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...