Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.

ABSTRACT:

Background

Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved.

Results

This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO)

SUBMITTER: Wan S 

PROVIDER: S-EPMC4765148 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature