Article

Identifying digenic disease genes using machine learning in the undiagnosed diseases network

2020-06-01

Abstract excerpt

<h4>ABSTRACT</h4> Rare diseases affect hundreds of millions of people worldwide, and diagnosing their genetic causes is challenging. The Undiagnosed Diseases Network (UDN) was formed in 2014 to identify and treat novel rare genetic diseases, and despite many successes, more than half of UDN patients remain undiagnosed. The central hypothesis of this work is that many unsolved rare genetic disorders are caused by...

Topics

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

Identifiers and source

Literature Corpus work: 4b914b98-be7f-5202-84fe-c438c59e2620
DOI: 10.1101/2020.05.31.125716

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Identifying digenic disease genes using machine learning in the undiagnosed diseases networkDOI 10.1101/2020.05.31.125716Read

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.Semantic proximity Read

Understanding mutational effects in digenic diseases.Semantic proximity Read

DIDA: A curated and annotated digenic diseases database.Semantic proximity Read

Digenic variant interpretation with hypothesis-driven explainable AISemantic proximity Read

Machine learning approaches to explore digenic inheritance.Semantic proximity Read

Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentationsSemantic proximity Read

Diagnosis and discovery: Insights from the NIH Undiagnosed Diseases ProgramSemantic proximity Read

Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentationsSemantic proximity Read

Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases.Semantic proximity Read

InpherNet provides attractive monogenic disease gene hypotheses using patient genes indirect neighborsSemantic proximity Read

Deciphering the digenic architecture of congenital heart disease using trio exome sequencing data.Semantic proximity Read

A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systemsSemantic proximity Read