.png){kind=icon}
返回
High-throughput deep learning variant effect prediction with Sequence UNET
.png){kind=icon}
OA
AI
DOI：10.1186/s13059-023-02948-3.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult to scale, including recent deep learning models. We introduce Sequence UNET, a highly scalable deep learning architecture that classifies and predicts variant frequency from sequence alone using multi-scale representations from a fully convolutional compression/expansion architecture. It achieves comparable pathogenicity prediction to recent methods. We demonstrate scalability by analysing 8.3B variants in 904,134 proteins detected through large-scale proteomics. Sequence UNET runs on modest hardware with a simple Python package.
Keyword:
Variant effect prediction
Deep learning
Mutation
PSSM
Pathogenicity
Machine learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
G
IF:
9.4
论文数:
6.4K
被引数:
7.3W
机构
引用论文
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVsdbNSFP v4: 人类非同义和剪接位点snv的转录本特异性功能预测和注释的综合数据库.png)
GENOME MEDICINEOAAI
GENOME MEDICINEIF11.2
OAAI分享收藏Role of conformational sampling in computing mutation-induced changes in protein structure and stability构象采样在计算突变引起的蛋白质结构和稳定性变化中的作用OAAI
OAAI分享收藏分享收藏Recurrent Neural Network for Predicting Transcription Factor Binding Sites预测转录因子结合位点的递归神经网络SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Deep generative models of genetic variation capture the effects of mutations遗传变异的深度生成模型捕捉了突变的影响NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏
查看更多内容