Going from where to why--interpretable prediction of protein subcellular localization.

ABSTRACT:

Motivation

Protein subcellular localization is pivotal in understanding a protein's function. Computational prediction of subcellular localization has become a viable alternative to experimental approaches. While current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations.

Results

We present YLoc, a novel method for predicting protein subcellular localization that addresses these issues. Due to its simple architecture, YLoc can identify the relevant features of a protein sequence contributing to its subcellular localization, e.g. localization signals or motifs relevant to protein sorting. We present several example applications where YLoc identifies the sequence

SUBMITTER: Briesemeister S 

PROVIDER: S-EPMC2859129 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature