Article

Integrative approaches to improve the informativeness of deep learning models for human complex diseases

2020-09-09

Abstract excerpt

Deep learning models have achieved great success in predicting genome-wide regulatory effects from DNA sequence, but recent work has reported that SNP annotations derived from these predictions contribute limited unique information for human complex disease. Here, we explore three integrative approaches to improve the disease informativeness of allelic-effect annotations (predicted difference between reference and...

Topics

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

Identifiers and source

Literature Corpus work: 8a8ca6d0-5d20-552a-9999-814454cca86d
DOI: 10.1101/2020.09.08.288563

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Integrative approaches to improve the informativeness of deep learning models for human complex diseasesDOI 10.1101/2020.09.08.288563Read

Evaluating the informativeness of deep learning annotations for human complex diseasesSemantic proximity Read

Evaluating the informativeness of deep learning annotations for human complex diseases.Semantic proximity Read

A machine-learning framework to characterize functional disease architectures and prioritize disease variantsSemantic proximity Read

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune diseaseSemantic proximity Read

A machine-learning framework to characterize functional disease architectures and prioritize disease variantsSemantic proximity Read

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease.Semantic proximity Read

Deep integrative models for large-scale human genomicsSemantic proximity Read

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.Semantic proximity Read

Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity.Semantic proximity Read

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common diseaseSemantic proximity Read

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signalsSemantic proximity Read

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learningSemantic proximity Read