Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning.

ABSTRACT:

Motivation

There is a long-term interest in the challenging task of finding translocated and mislocated cancer biomarker proteins. Bioimages of subcellular protein distribution are new data sources which have attracted much attention in recent years because of their intuitive and detailed descriptions of protein distribution. However, automated methods in large-scale biomarker screening suffer significantly from the lack of subcellular location annotations for bioimages from cancer tissues. The transfer prediction idea of applying models trained on normal tissue proteins to predict the subcellular locations of cancerous ones is arbitrary because the protein distribution patterns may differ in normal and cancerous states.

Results

We developed a new semi-supervised protocol th

SUBMITTER: Xu YY 

PROVIDER: S-EPMC4382902 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature