Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields

doi:10.1109/TCBB.2019.2897683

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields

2019-09-01

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DOI：10.1109/TCBB.2019.2897683

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The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.

Keywords:

Proteins

Markov processes

Predictive models

Labeling

Biological tissues

Diseases

Graphical models

Human protein subcellular localization

protein-protein interaction

tissue-specific analysis

multi-label markov random field

imbalanced multi-label classification

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields

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