Network based subcellular localization prediction for multi-label proteins

Ananda Mohan Mondal; Jhih Rong Lin; Jianjun Hu

doi:10.1109/BIBMW.2011.6112416

Network based subcellular localization prediction for multi-label proteins

Ananda Mohan Mondal, Jhih Rong Lin, Jianjun Hu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

Many proteins are sorted to multiple subcellular localizations within the cell. However, computational prediction of multi-location proteins remains a challenging task. Here we applied a logistic regression and diffusion kernel based algorithm NetLoc for predicting multiplex proteins and explored its capability and limitations. Experiment shows that the overall and true success rates for physical protein-protein interaction network are 65% and 41% respectively, and for mixed PPI network these values are 88% and 75% respectively. Our study also showed that the performance of NetLoc in predicting protein localization is limited by the network characteristics such as ratio of the number of co-localized protein-protein interactions (coPPI) to the number of non-co-localized PPI (ncPPI) and the density of annotated coPPI in the network. For a given network with a specific number of proteins, NetLoc performance increases with higher coPPI/ncPPI ratio and higher density of annotated coPPI.

Original language	English (US)
Title of host publication	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Pages	473-480
Number of pages	8
DOIs	https://doi.org/10.1109/BIBMW.2011.6112416
State	Published - Dec 1 2011
Externally published	Yes
Event	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 - Atlanta, GA, United States Duration: Nov 12 2011 → Nov 15 2011

Publication series

Name	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Conference

Conference	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Country/Territory	United States
City	Atlanta, GA
Period	11/12/11 → 11/15/11

Keywords

NetLoc
bioinformatics
data mining
diffusion kernel
multi-label proteins
multiplex protein localization
multiplex proteins
network based protein localization
protein localization
protein subcellular localization

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.1109/BIBMW.2011.6112416

Cite this

Mondal, A. M., Lin, J. R., & Hu, J. (2011). Network based subcellular localization prediction for multi-label proteins. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 (pp. 473-480). [6112416] (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011). https://doi.org/10.1109/BIBMW.2011.6112416

Network based subcellular localization prediction for multi-label proteins. / Mondal, Ananda Mohan; Lin, Jhih Rong; Hu, Jianjun.

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. p. 473-480 6112416 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mondal, AM, Lin, JR & Hu, J 2011, Network based subcellular localization prediction for multi-label proteins. in 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011., 6112416, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, pp. 473-480, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, Atlanta, GA, United States, 11/12/11. https://doi.org/10.1109/BIBMW.2011.6112416

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title = "Network based subcellular localization prediction for multi-label proteins",

abstract = "Many proteins are sorted to multiple subcellular localizations within the cell. However, computational prediction of multi-location proteins remains a challenging task. Here we applied a logistic regression and diffusion kernel based algorithm NetLoc for predicting multiplex proteins and explored its capability and limitations. Experiment shows that the overall and true success rates for physical protein-protein interaction network are 65% and 41% respectively, and for mixed PPI network these values are 88% and 75% respectively. Our study also showed that the performance of NetLoc in predicting protein localization is limited by the network characteristics such as ratio of the number of co-localized protein-protein interactions (coPPI) to the number of non-co-localized PPI (ncPPI) and the density of annotated coPPI in the network. For a given network with a specific number of proteins, NetLoc performance increases with higher coPPI/ncPPI ratio and higher density of annotated coPPI.",

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author = "Mondal, {Ananda Mohan} and Lin, {Jhih Rong} and Jianjun Hu",

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N2 - Many proteins are sorted to multiple subcellular localizations within the cell. However, computational prediction of multi-location proteins remains a challenging task. Here we applied a logistic regression and diffusion kernel based algorithm NetLoc for predicting multiplex proteins and explored its capability and limitations. Experiment shows that the overall and true success rates for physical protein-protein interaction network are 65% and 41% respectively, and for mixed PPI network these values are 88% and 75% respectively. Our study also showed that the performance of NetLoc in predicting protein localization is limited by the network characteristics such as ratio of the number of co-localized protein-protein interactions (coPPI) to the number of non-co-localized PPI (ncPPI) and the density of annotated coPPI in the network. For a given network with a specific number of proteins, NetLoc performance increases with higher coPPI/ncPPI ratio and higher density of annotated coPPI.

AB - Many proteins are sorted to multiple subcellular localizations within the cell. However, computational prediction of multi-location proteins remains a challenging task. Here we applied a logistic regression and diffusion kernel based algorithm NetLoc for predicting multiplex proteins and explored its capability and limitations. Experiment shows that the overall and true success rates for physical protein-protein interaction network are 65% and 41% respectively, and for mixed PPI network these values are 88% and 75% respectively. Our study also showed that the performance of NetLoc in predicting protein localization is limited by the network characteristics such as ratio of the number of co-localized protein-protein interactions (coPPI) to the number of non-co-localized PPI (ncPPI) and the density of annotated coPPI in the network. For a given network with a specific number of proteins, NetLoc performance increases with higher coPPI/ncPPI ratio and higher density of annotated coPPI.

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Network based subcellular localization prediction for multi-label proteins

Abstract

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Keywords

ASJC Scopus subject areas

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