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 - 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). Article 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

@inproceedings{7e0f7af4b8074f45a2473bd59b7b99c6,

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.",

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

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

year = "2011",

doi = "10.1109/BIBMW.2011.6112416",

language = "English (US)",

isbn = "9781457716133",

series = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

pages = "473--480",

booktitle = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

note = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 ; Conference date: 12-11-2011 Through 15-11-2011",

}

TY - GEN

T1 - Network based subcellular localization prediction for multi-label proteins

AU - Mondal, Ananda Mohan

AU - Lin, Jhih Rong

AU - Hu, Jianjun

PY - 2011

Y1 - 2011

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.

KW - NetLoc

KW - bioinformatics

KW - data mining

KW - diffusion kernel

KW - multi-label proteins

KW - multiplex protein localization

KW - multiplex proteins

KW - network based protein localization

KW - protein localization

KW - protein subcellular localization

UR - http://www.scopus.com/inward/record.url?scp=84862929318&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862929318&partnerID=8YFLogxK

U2 - 10.1109/BIBMW.2011.6112416

DO - 10.1109/BIBMW.2011.6112416

M3 - Conference contribution

AN - SCOPUS:84862929318

SN - 9781457716133

T3 - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

SP - 473

EP - 480

BT - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

T2 - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Y2 - 12 November 2011 through 15 November 2011

ER -

Network based subcellular localization prediction for multi-label proteins

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this