例如:"lncRNA", "apoptosis", "WRKY"

McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.

Genome Biol.2017 Oct 26;18(1):199
{{ author.authorName }}{{getOrganisationIndexOf(author)}} {{ author.authorName }}{{getOrganisationIndexOf(author)}}
{{ author.authorName }}{{getOrganisationIndexOf(author)}} {{ author.authorName }}{{getOrganisationIndexOf(author)}}
+ et al

[No authors listed]

Author information
  • {{index+1}} {{ organisation }}

摘要


Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features. Predicted expression patterns were 73-98% accurate, predicted assignments showed strong Hi-C interaction enrichment, enhancer-associated histone modifications were evident, and known functional motifs were recovered. Our model provides a general framework to link globally identified enhancers to targets and contributes to deciphering the regulatory genome.

KEYWORDS: {{ getKeywords(articleDetailText.words) }}

基因功能


  • {{$index+1}}.{{ gene }}

图表


原始数据


 保存测序数据
Sample name
Organism Experiment title Sample type Library instrument Attributes
{{attr}}
{{ dataList.sampleTitle }}
{{ dataList.organism }} {{ dataList.expermentTitle }} {{ dataList.sampleType }} {{ dataList.libraryInstrument }} {{ showAttributeName(index,attr,dataList.attributes) }}

文献解读