Augmented transitive relationships in direct protein-protein interaction prediction

Yi Tsung Tang, Hung-Yu Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The prediction of new protein-protein interactions is important to the discovery of the currently unknown function of various biological pathways. In addition, many databases of protein-protein interactions contain different types of interactions, including protein associations, physical protein associations and direct protein interactions. There are only a few studies that consider the issues inherent to the prediction of direct protein-protein interactions, that is, interactions between proteins that are actually in direct physical contact and are listed in known protein interaction databases. Predicting these interactions is a crucial and challenging task. Therefore, it is increasingly important to discover not only protein associations but also direct interactions. Many studies have predicted protein-protein interactions directly, by using biological features such as Gene Ontology (GO) functions and protein structural domains of two proteins with unknown interactions. In this article, we proposed an augmented transitive relationships predictor (ATRP), a new method of predicting potential direct protein-protein interactions by using transitive relationships and annotations of protein interactions. Our results demonstrate that ATRP can effectively predict unknown direct protein-protein interactions from existing protein interaction relationships. The average accuracy of this method outperformed GO-based prediction methods by a factor ranging from 28% to 62%.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011
Pages530-535
Number of pages6
DOIs
Publication statusPublished - 2011 Sep 19
Event5th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011 - Seoul, Korea, Republic of
Duration: 2011 Jun 302011 Jul 2

Publication series

NameProceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011

Other

Other5th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011
CountryKorea, Republic of
CitySeoul
Period11-06-3011-07-02

Fingerprint

Proteins
Ontology
Genes

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

Cite this

Tang, Y. T., & Kao, H-Y. (2011). Augmented transitive relationships in direct protein-protein interaction prediction. In Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011 (pp. 530-535). [5989065] (Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011). https://doi.org/10.1109/CISIS.2011.87
Tang, Yi Tsung ; Kao, Hung-Yu. / Augmented transitive relationships in direct protein-protein interaction prediction. Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011. 2011. pp. 530-535 (Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011).
@inproceedings{359c2fc7247b43488857909ae324e64f,
title = "Augmented transitive relationships in direct protein-protein interaction prediction",
abstract = "The prediction of new protein-protein interactions is important to the discovery of the currently unknown function of various biological pathways. In addition, many databases of protein-protein interactions contain different types of interactions, including protein associations, physical protein associations and direct protein interactions. There are only a few studies that consider the issues inherent to the prediction of direct protein-protein interactions, that is, interactions between proteins that are actually in direct physical contact and are listed in known protein interaction databases. Predicting these interactions is a crucial and challenging task. Therefore, it is increasingly important to discover not only protein associations but also direct interactions. Many studies have predicted protein-protein interactions directly, by using biological features such as Gene Ontology (GO) functions and protein structural domains of two proteins with unknown interactions. In this article, we proposed an augmented transitive relationships predictor (ATRP), a new method of predicting potential direct protein-protein interactions by using transitive relationships and annotations of protein interactions. Our results demonstrate that ATRP can effectively predict unknown direct protein-protein interactions from existing protein interaction relationships. The average accuracy of this method outperformed GO-based prediction methods by a factor ranging from 28{\%} to 62{\%}.",
author = "Tang, {Yi Tsung} and Hung-Yu Kao",
year = "2011",
month = "9",
day = "19",
doi = "10.1109/CISIS.2011.87",
language = "English",
isbn = "9780769543734",
series = "Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011",
pages = "530--535",
booktitle = "Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011",

}

Tang, YT & Kao, H-Y 2011, Augmented transitive relationships in direct protein-protein interaction prediction. in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011., 5989065, Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011, pp. 530-535, 5th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011, Seoul, Korea, Republic of, 11-06-30. https://doi.org/10.1109/CISIS.2011.87

Augmented transitive relationships in direct protein-protein interaction prediction. / Tang, Yi Tsung; Kao, Hung-Yu.

Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011. 2011. p. 530-535 5989065 (Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Augmented transitive relationships in direct protein-protein interaction prediction

AU - Tang, Yi Tsung

AU - Kao, Hung-Yu

PY - 2011/9/19

Y1 - 2011/9/19

N2 - The prediction of new protein-protein interactions is important to the discovery of the currently unknown function of various biological pathways. In addition, many databases of protein-protein interactions contain different types of interactions, including protein associations, physical protein associations and direct protein interactions. There are only a few studies that consider the issues inherent to the prediction of direct protein-protein interactions, that is, interactions between proteins that are actually in direct physical contact and are listed in known protein interaction databases. Predicting these interactions is a crucial and challenging task. Therefore, it is increasingly important to discover not only protein associations but also direct interactions. Many studies have predicted protein-protein interactions directly, by using biological features such as Gene Ontology (GO) functions and protein structural domains of two proteins with unknown interactions. In this article, we proposed an augmented transitive relationships predictor (ATRP), a new method of predicting potential direct protein-protein interactions by using transitive relationships and annotations of protein interactions. Our results demonstrate that ATRP can effectively predict unknown direct protein-protein interactions from existing protein interaction relationships. The average accuracy of this method outperformed GO-based prediction methods by a factor ranging from 28% to 62%.

AB - The prediction of new protein-protein interactions is important to the discovery of the currently unknown function of various biological pathways. In addition, many databases of protein-protein interactions contain different types of interactions, including protein associations, physical protein associations and direct protein interactions. There are only a few studies that consider the issues inherent to the prediction of direct protein-protein interactions, that is, interactions between proteins that are actually in direct physical contact and are listed in known protein interaction databases. Predicting these interactions is a crucial and challenging task. Therefore, it is increasingly important to discover not only protein associations but also direct interactions. Many studies have predicted protein-protein interactions directly, by using biological features such as Gene Ontology (GO) functions and protein structural domains of two proteins with unknown interactions. In this article, we proposed an augmented transitive relationships predictor (ATRP), a new method of predicting potential direct protein-protein interactions by using transitive relationships and annotations of protein interactions. Our results demonstrate that ATRP can effectively predict unknown direct protein-protein interactions from existing protein interaction relationships. The average accuracy of this method outperformed GO-based prediction methods by a factor ranging from 28% to 62%.

UR - http://www.scopus.com/inward/record.url?scp=80052726781&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80052726781&partnerID=8YFLogxK

U2 - 10.1109/CISIS.2011.87

DO - 10.1109/CISIS.2011.87

M3 - Conference contribution

AN - SCOPUS:80052726781

SN - 9780769543734

T3 - Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011

SP - 530

EP - 535

BT - Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011

ER -

Tang YT, Kao H-Y. Augmented transitive relationships in direct protein-protein interaction prediction. In Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011. 2011. p. 530-535. 5989065. (Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011). https://doi.org/10.1109/CISIS.2011.87