.png){kind=icon}
返回
A pitfall for machine learning methods aiming to predict across cell types
.png){kind=icon}
OA
AI
DOI：10.1186/s13059-020-02177-y.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to perform well by effectively memorizing the average activity associated with each locus across the training cell types. We demonstrate this phenomenon in the context of predicting gene expression and chromatin domain boundaries, and we suggest methods to diagnose and avoid the pitfall. We anticipate that, as more data becomes available, future projects will increasingly risk suffering from this issue.
Keyword:
Machine learning
Epigenomics
Genomics
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
Genome-wide prediction of transcription factor binding sites using an integrated model.png)
GENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏Distinct 5-methylcytosine profiles in poly(A) RNA from mouse embryonic stem cells and brainGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor DecompositionNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Cation ordering and structural variations with temperature in MgAl2O4spinel: An X-ray single-crystal studyMgAl2O4 尖晶石中阳离子有序性和结构随温度的变化: x射线单晶研究
PREAI
分享收藏DEEP: a general computational framework for predicting enhancersDEEP: 预测增强剂的通用计算框架NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏In situ click chemistry generation of cyclooxygenase-2 inhibitorscyclooxygenase-2抑制剂的原位点击化学生成NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Unsupervised pattern discovery in human chromatin structure through genomic segmentationNATURE METHODS
OAAI
NATURE METHODSIF32.1
OAAI分享收藏
查看更多内容