Determining the subcellular location of new proteins from microscope images using local features.

ABSTRACT:

Motivation

Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified.

Results

Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on. We therefore developed and evaluated additional approaches, incorpor

SUBMITTER: Coelho LP 

PROVIDER: S-EPMC3753569 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature