Article

Cell type-specific interpretation of noncoding variants using deep learning-based methods

2022-01-01

Abstract excerpt

Interpretation of non-coding genomic variants is one of the most important challenges in human genetics. Machine learning methods have emerged recently as a powerful tool to solve this problem. State-of-the-art approaches allow prediction of transcriptional and epigenetic effects caused by non-coding mutations. However, these approaches require specific experimental data for training and can not generalize across...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 3da68f1c-908a-59f6-a3cd-cb9f9b8da836
DOI: 10.1101/2021.12.31.474623

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Cell type-specific interpretation of noncoding variants using deep learning-based methodsDOI 10.1101/2021.12.31.474623Read

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Semantic proximity Read

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.Semantic proximity Read

An analytical framework for decoding cell type-specific genetic variation of gene regulation.Semantic proximity Read

Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on EpigenomeSemantic proximity Read

Decoding sequence determinants of gene expression in diverse cellular and disease statesSemantic proximity Read

Current genomic deep learning models display decreased performance in cell type specific accessible regionsSemantic proximity Read

Predicting the effect of non-coding mutations on single-cell DNA methylation using deep learningSemantic proximity Read

CREP: Cis-Regulatory Element Predictor Based on Fine-Tuned EnformerSemantic proximity Read

scPrediXcan integrates advances in deep learning and single-cell data into a powerful cell-type–specific transcriptome-wide association study frameworkSemantic proximity Read

Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Semantic proximity Read

Interpreting non-coding disease-associated human variants using single-cell epigenomics.Semantic proximity Read

A sequence-based global map of regulatory activity for deciphering human geneticsSemantic proximity Read