Predicting regulatory variants with composite statistic | Zendy

Mulin Jun Li | Zendy; Zhicheng Pan | Zendy; Zipeng Liu | Zendy; Jiexing Wu | Zendy; Panwen Wang | Zendy; Yun Zhu | Zendy; Feng Xu | Zendy; Zhengyuan Xia | Zendy; Pak C. Sham | Zendy; JeanPierre Kocher | Zendy; Miaoxin Li | Zendy; Jun S. Liu | Zendy; Junwen Wang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Predicting regulatory variants with composite statistic

Author(s) -

Mulin Jun Li,

Zhicheng Pan,

Zipeng Liu,

Jiexing Wu,

Panwen Wang,

Yun Zhu,

Feng Xu,

Zhengyuan Xia,

Pak C. Sham,

JeanPierre Kocher,

Miaoxin Li,

Jun S. Liu,

Junwen Wang

Publication year - 2016

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/btw288

Subject(s) - computer science , statistic , annotation , prioritization , machine learning , coding (social sciences) , data mining , computational biology , artificial intelligence , biology , statistics , mathematics , management science , economics

Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction.

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