MNESEDA: A prior-guided subgraph representation learning framework for predicting disease-related enhancers-Reference-Cited by-同舟云学术

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MNESEDA: A prior-guided subgraph representation learning framework for predicting disease-related enhancers

Published:2024-06 Issue: Volume:294 Page:111734
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Xu Jinsheng^ORCID,Sun Weicheng,Li Kai,Zhang Wenxue,Zhang Weihan,Zeng Yongbin,Wong Leon,Zhang Ping^ORCID

Publisher

Elsevier BV

Reference69 articles.

1. Enhancer redundancy in development and disease;Kvon;Nat. Rev. Genet,2021

2. Enhancer variants: evaluating functions in common disease;Corradin;Genome. Med,2014

3. Transcriptional enhancers: from properties to genome-wide predictions;Shlyueva;Nat. Rev. Genet,2014

4. Towards a comprehensive catalogue of validated and target-linked human enhancers;Gasperini;Nat. Rev. Genet,2020

5. Enhancer control of transcriptional bursting;Fukaya;Cell,2016

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