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Genetic Variation

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Predicting pathogenic protein variants.

Science (New York, N.Y.)
Machine-learning algorithm uses structure prediction to spot disease-causing mutations.

Personal transcriptome variation is poorly explained by current genomic deep learning models.

Nature genetics
Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the refer...

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.

PloS one
BACKGROUND: Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Asso...

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions.

Nucleic acids research
Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of th...

Deep learning the cis-regulatory code for gene expression in selected model plants.

Nature communications
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predict...

Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation.

Journal of chemical information and modeling
Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully...

TSVM: Transfer Support Vector Machine for Predicting MPRA Validated Regulatory Variants.

IEEE/ACM transactions on computational biology and bioinformatics
Genome-wide association studies have shown that common genetic variants associated with complex diseases are mostly located in non-coding regions, which may not be causal. In addition, the limited number of validated non-coding functional variants ma...

Artificial intelligence enables unified analysis of historical and landscape influences on genetic diversity.

Molecular phylogenetics and evolution
While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with inferring how environmental conditions and landscape features impact neutral intraspecific diversit...

Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.

Nature genetics
Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico score for CAD built using machine learning and clinical data in electronic health records captures disea...

A comprehensive review of deep learning-based variant calling methods.

Briefings in functional genomics
Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspect...