Promoter analysis and prediction in the human genome using sequence-based deep learning models | Zendy

Ramzan Umarov | Zendy; Hiroyuki Kuwahara | Zendy; Yu Li | Zendy; Xin Gao | Zendy; Victor Solovyev | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Promoter analysis and prediction in the human genome using sequence-based deep learning models

Author(s) -

Ramzan Umarov,

Hiroyuki Kuwahara,

Yu Li,

Xin Gao,

Victor Solovyev

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty1068

Subject(s) - promoter , discriminative model , computational biology , computer science , identification (biology) , deep learning , human genome , dna binding site , genome , artificial intelligence , biology , machine learning , gene , genetics , gene expression , botany

Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliable tool to analyze long genomic sequences.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research