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

Zhu L, Hofestädt R, Ester M (2019)
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 16(5): 1471-1482.

Zeitschriftenaufsatz | Veröffentlicht| Englisch
 
Download
Es wurde kein Volltext hochgeladen. Nur Publikationsnachweis!
Autor/in
Abstract / Bemerkung
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.
Stichworte
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
Erscheinungsjahr
2019
Zeitschriftentitel
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Band
16
Ausgabe
5
Seite(n)
1471-1482
ISSN
1545-5963
eISSN
1557-9964
Page URI
https://pub.uni-bielefeld.de/record/2938754

Zitieren

Zhu L, Hofestädt R, Ester M. Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 2019;16(5):1471-1482.
Zhu, L., Hofestädt, R., & Ester, M. (2019). Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 16(5), 1471-1482. doi:10.1109/TCBB.2019.2897683
Zhu, L., Hofestädt, R., and Ester, M. (2019). Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 16, 1471-1482.
Zhu, L., Hofestädt, R., & Ester, M., 2019. Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 16(5), p 1471-1482.
L. Zhu, R. Hofestädt, and M. Ester, “Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields”, IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, vol. 16, 2019, pp. 1471-1482.
Zhu, L., Hofestädt, R., Ester, M.: Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 16, 1471-1482 (2019).
Zhu, Lu, Hofestädt, Ralf, and Ester, Martin. “Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields”. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 16.5 (2019): 1471-1482.

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 30736003
PubMed | Europe PMC

Suchen in

Google Scholar