CNet: a multi-omics approach to detecting clinically associated, combinatory genomic signatures | Zendy

Peilin Jia | Zendy; Guangsheng Pei | Zendy; Zhongming Zhao | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

CNet: a multi-omics approach to detecting clinically associated, combinatory genomic signatures

Author(s) -

Peilin Jia,

Guangsheng Pei,

Zhongming Zhao

Publication year - 2019

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/btz441

Subject(s) - genomics , computational biology , categorical variable , genome , computer science , biology , genetics , gene , machine learning

Genome-wide multi-omics profiling of complex diseases provides valuable resources and opportunities to discover associations between various measures of genes and diseases. Currently, a pressing challenge is how to effectively detect functional genes associated with or causing phenotypic outcomes. We developed CNet to identify groups of genomic signatures whose combinatory effect is significantly associated with clinical and phenotypical outcomes.

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