Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes | Zendy

Shane T. Jensen | Zendy; Lei Shen | Zendy; Jun S. Liu | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes

Author(s) -

Shane T. Jensen,

Lei Shen,

Jun S. Liu

Publication year - 2005

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/bti628

Subject(s) - motif (music) , cluster analysis , computational biology , sequence motif , gene , hierarchical clustering , phylogenetic tree , inference , biology , genetics , data mining , computer science , artificial intelligence , physics , acoustics

We present a sequence-based framework and algorithm PHYLOCLUS for predicting co-regulated genes. In our approach, de novo discovery methods are used to find motifs conserved by evolution and then a Bayesian hierarchical clustering model is used to cluster these motifs, thereby grouping together genes that are putatively co-regulated. Our clustering procedure allows both the number of clusters and the motif width within each cluster to be unknown.

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