Context-specific independence mixture modeling for positional weight matrices | Zendy

Benjamin Georgi | Zendy; Alexander Schliep | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Context-specific independence mixture modeling for positional weight matrices

Author(s) -

Benjamin Georgi,

Alexander Schliep

Publication year - 2006

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/btl249

Subject(s) - overfitting , robustness (evolution) , computer science , context (archaeology) , biological system , pattern recognition (psychology) , artificial intelligence , algorithm , mathematics , statistics , biology , genetics , artificial neural network , paleontology , gene

A positional weight matrix (PWM) is a statistical representation of the binding pattern of a transcription factor estimated from known binding site sequences. Previous studies showed that for factors which bind to divergent binding sites, mixtures of multiple PWMs increase performance. However, estimating a conventional mixture distribution for each position will in many cases cause overfitting.

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