Long-range interaction analysis using principal component analysis

Peng Chen; Bing Wang; Hau-San Wong; De-Shuang Huang

doi:10.1109/IJCNN.2006.247054

Long-range interaction analysis using principal component analysis

Peng Chen
, Bing Wang
, Hau-San Wong
, De-Shuang Huang^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

This paper analyzes the long-range interactions, which plays a fundamental and important role in many biologic fields, between residues in protein using principal component analysis (PCA). Firstly, one angular coordinate system of long-range interaction regions is constructed conveniently. Afterwards, a matrix of the angular values of residues can be analyzed by principal component analysis technique. Projecting the angular matrix onto its eigenvectors, it can be found that the projection is to satisfy Boltzmann distribution. By analyzing the thermodynamic environment of the interaction region and scaling the interaction regions, it can be concluded that the distribution of long-range interactions may also be obtained and as a result applied in prediction of contact map. © 2006 IEEE.

Original language	English
Title of host publication	The 2006 IEEE International Joint Conference on Neural Network Proceedings
Publisher	IEEE
Pages	2331-2336
ISBN (Print)	0780394909, 9780780394902
DOIs	https://doi.org/10.1109/IJCNN.2006.247054
Publication status	Published - 2006
Event	2006 International Joint Conference on Neural Networks (IJCNN '06) - Vancouver, BC, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Publication series

Name
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2006 International Joint Conference on Neural Networks (IJCNN '06)
Place	Canada
City	Vancouver, BC
Period	16/07/06 → 21/07/06

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@inproceedings{148294a7845e4322ab1bdd599d3f1ff2,

title = "Long-range interaction analysis using principal component analysis",

abstract = "This paper analyzes the long-range interactions, which plays a fundamental and important role in many biologic fields, between residues in protein using principal component analysis (PCA). Firstly, one angular coordinate system of long-range interaction regions is constructed conveniently. Afterwards, a matrix of the angular values of residues can be analyzed by principal component analysis technique. Projecting the angular matrix onto its eigenvectors, it can be found that the projection is to satisfy Boltzmann distribution. By analyzing the thermodynamic environment of the interaction region and scaling the interaction regions, it can be concluded that the distribution of long-range interactions may also be obtained and as a result applied in prediction of contact map. {\textcopyright} 2006 IEEE.",

author = "Peng Chen and Bing Wang and Hau-San Wong and De-Shuang Huang",

year = "2006",

doi = "10.1109/IJCNN.2006.247054",

language = "English",

isbn = "0780394909",

publisher = "IEEE",

pages = "2331--2336",

booktitle = "The 2006 IEEE International Joint Conference on Neural Network Proceedings",

address = "United States",

note = "2006 International Joint Conference on Neural Networks (IJCNN '06) ; Conference date: 16-07-2006 Through 21-07-2006",

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TY - GEN

T1 - Long-range interaction analysis using principal component analysis

AU - Chen, Peng

AU - Wang, Bing

AU - Wong, Hau-San

AU - Huang, De-Shuang

PY - 2006

Y1 - 2006

N2 - This paper analyzes the long-range interactions, which plays a fundamental and important role in many biologic fields, between residues in protein using principal component analysis (PCA). Firstly, one angular coordinate system of long-range interaction regions is constructed conveniently. Afterwards, a matrix of the angular values of residues can be analyzed by principal component analysis technique. Projecting the angular matrix onto its eigenvectors, it can be found that the projection is to satisfy Boltzmann distribution. By analyzing the thermodynamic environment of the interaction region and scaling the interaction regions, it can be concluded that the distribution of long-range interactions may also be obtained and as a result applied in prediction of contact map. © 2006 IEEE.

AB - This paper analyzes the long-range interactions, which plays a fundamental and important role in many biologic fields, between residues in protein using principal component analysis (PCA). Firstly, one angular coordinate system of long-range interaction regions is constructed conveniently. Afterwards, a matrix of the angular values of residues can be analyzed by principal component analysis technique. Projecting the angular matrix onto its eigenvectors, it can be found that the projection is to satisfy Boltzmann distribution. By analyzing the thermodynamic environment of the interaction region and scaling the interaction regions, it can be concluded that the distribution of long-range interactions may also be obtained and as a result applied in prediction of contact map. © 2006 IEEE.

UR - https://www.scopus.com/pages/publications/40649089687

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-40649089687&origin=recordpage

U2 - 10.1109/IJCNN.2006.247054

DO - 10.1109/IJCNN.2006.247054

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0780394909

SN - 9780780394902

SP - 2331

EP - 2336

BT - The 2006 IEEE International Joint Conference on Neural Network Proceedings

PB - IEEE

T2 - 2006 International Joint Conference on Neural Networks (IJCNN '06)

Y2 - 16 July 2006 through 21 July 2006

ER -

Long-range interaction analysis using principal component analysis

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