Here, we present a new method (LocNuclei) that predicts nuclear substructures from sequence alone.  LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs).  This suggested that the LocNuclei predictions carry important information about function.  The source code and data sets are available through GitHub: https://github.com/Rostlab/LocNuclei .


 LocNuclei predicts subnuclear compartments and traveler proteins accurately. 
LocNuclei
LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs). 
Support Vector Machines (SVMs)
High performance was achieved by implicitly leveraging a large biological knowledge-base in creating predictions by homology-based inference through BLAST. 
BLAST
A Gene Ontology (GO) analysis of the enrichment of biological processes revealed that the predicted sub-nuclear compartments matched the expected functionality. 
Gene Ontology (GO)