A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels

Lavneet Singh; Girija Chetty; Dharmendra Sharma

doi:10.1007/978-3-642-33065-0_31

A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels

Lavneet Singh, Girija Chetty, Dharmendra Sharma

Research output: A Conference proceeding or a Chapter in Book › Conference contribution

1 Citation (Scopus)

Abstract

In the area of bio-informatics, large amount of data is harvested with functional and genetic features of proteins. The structure of protein plays an important role in its biological and genetic functions. In this study, we propose a protein structure prediction scheme based novel learning algorithms – the extreme learning machine and the Support Vector Machine using multiple kernel learning, The experimental validation of the proposed approach on a publicly available protein data set shows a significant improvement in performance of the proposed approach in terms of accuracy of classification of protein folds using multiple kernels where multiple heterogeneous feature space data are available. The proposed method provides the higher recognition ratio as compared to other methods reported in previous studies.

Original language	English
Title of host publication	International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP)
Subtitle of host publication	Lecture Notes in Computer Science
Editors	Yang Xiang, Ivan Stojmenovic, Bernady O Apduhan, Guojun Wang, Koji Nakano, Albert Zomaya
Place of Publication	Berlin Heidelberg
Publisher	Springer
Pages	292-299
Number of pages	8
Volume	7440
ISBN (Electronic)	9783642330650
ISBN (Print)	9783642330643
DOIs	https://doi.org/10.1007/978-3-642-33065-0_31
Publication status	Published - 2012
Event	12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012: ICA3PP 2012 - Fukuoka, Fukuoka, Japan Duration: 4 Sept 2012 → 7 Sept 2012 http://nsclab.org/ica3pp12/

Conference

Conference	12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012
Abbreviated title	ICA3PP 2012
Country/Territory	Japan
City	Fukuoka
Period	4/09/12 → 7/09/12
Other	ICA3PP 2012 is the 12th in this series of conferences started in 1995 that are devoted to algorithms and architectures for parallel processing. ICA3PP is now recognized as the main regular event of the world that is covering the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems. As applications of computing systems have permeated in every aspects of daily life, the power of computing system has become increasingly critical. This conference provides a forum for countries around the world to exchange ideas for improving the computation power of computing systems. Following the traditions of the previous successful ICA3PP conferences held in Hangzhou, Brisbane, Singapore, Melbourne, Hong Kong, Beijing, Cyprus, Taipei, Busan, and Melbourne, ICA3PP 2012 will be held in Fukuoka, Japan. The objective of ICA3PP 2012 is to bring together researchers and practitioners from academia, industry and governments to advance the theories and technologies in parallel and distributed computing. ICA3PP 2012 will focus on two broad areas of parallel and distributed computing, i.e., architectures, algorithms and networks, and systems and applications. The conference of ICA3PP 2012 will be organized by Kyushu Sangyo University, Japan
Internet address	http://nsclab.org/ica3pp12/

Access to Document

10.1007/978-3-642-33065-0_31

Cite this

Singh, L., Chetty, G., & Sharma, D. (2012). A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels. In Y. Xiang, I. Stojmenovic, B. O. Apduhan, G. Wang, K. Nakano, & A. Zomaya (Eds.), International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP): Lecture Notes in Computer Science (Vol. 7440, pp. 292-299). Springer. https://doi.org/10.1007/978-3-642-33065-0_31

Singh, Lavneet ; Chetty, Girija ; Sharma, Dharmendra. / A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels. International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP): Lecture Notes in Computer Science. editor / Yang Xiang ; Ivan Stojmenovic ; Bernady O Apduhan ; Guojun Wang ; Koji Nakano ; Albert Zomaya. Vol. 7440 Berlin Heidelberg : Springer, 2012. pp. 292-299

@inproceedings{efdd67d5e1a9449bb48d5fdeb3274613,

title = "A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels",

abstract = "In the area of bio-informatics, large amount of data is harvested with functional and genetic features of proteins. The structure of protein plays an important role in its biological and genetic functions. In this study, we propose a protein structure prediction scheme based novel learning algorithms – the extreme learning machine and the Support Vector Machine using multiple kernel learning, The experimental validation of the proposed approach on a publicly available protein data set shows a significant improvement in performance of the proposed approach in terms of accuracy of classification of protein folds using multiple kernels where multiple heterogeneous feature space data are available. The proposed method provides the higher recognition ratio as compared to other methods reported in previous studies.",

author = "Lavneet Singh and Girija Chetty and Dharmendra Sharma",

year = "2012",

doi = "10.1007/978-3-642-33065-0_31",

language = "English",

isbn = "9783642330643",

volume = "7440",

pages = "292--299",

editor = "Yang Xiang and Ivan Stojmenovic and Apduhan, {Bernady O} and Guojun Wang and Koji Nakano and Albert Zomaya",

booktitle = "International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP)",

publisher = "Springer",

address = "Netherlands",

note = "12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012 : ICA3PP 2012, ICA3PP 2012 ; Conference date: 04-09-2012 Through 07-09-2012",

url = "http://nsclab.org/ica3pp12/",

}

Singh, L, Chetty, G & Sharma, D 2012, A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels. in Y Xiang, I Stojmenovic, BO Apduhan, G Wang, K Nakano & A Zomaya (eds), International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP): Lecture Notes in Computer Science. vol. 7440, Springer, Berlin Heidelberg, pp. 292-299, 12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012, Fukuoka, Japan, 4/09/12. https://doi.org/10.1007/978-3-642-33065-0_31

A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels. / Singh, Lavneet; Chetty, Girija ; Sharma, Dharmendra.
International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP): Lecture Notes in Computer Science. ed. / Yang Xiang; Ivan Stojmenovic; Bernady O Apduhan; Guojun Wang; Koji Nakano; Albert Zomaya. Vol. 7440 Berlin Heidelberg: Springer, 2012. p. 292-299.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution

TY - GEN

T1 - A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels

AU - Singh, Lavneet

AU - Chetty, Girija

AU - Sharma, Dharmendra

PY - 2012

Y1 - 2012

N2 - In the area of bio-informatics, large amount of data is harvested with functional and genetic features of proteins. The structure of protein plays an important role in its biological and genetic functions. In this study, we propose a protein structure prediction scheme based novel learning algorithms – the extreme learning machine and the Support Vector Machine using multiple kernel learning, The experimental validation of the proposed approach on a publicly available protein data set shows a significant improvement in performance of the proposed approach in terms of accuracy of classification of protein folds using multiple kernels where multiple heterogeneous feature space data are available. The proposed method provides the higher recognition ratio as compared to other methods reported in previous studies.

AB - In the area of bio-informatics, large amount of data is harvested with functional and genetic features of proteins. The structure of protein plays an important role in its biological and genetic functions. In this study, we propose a protein structure prediction scheme based novel learning algorithms – the extreme learning machine and the Support Vector Machine using multiple kernel learning, The experimental validation of the proposed approach on a publicly available protein data set shows a significant improvement in performance of the proposed approach in terms of accuracy of classification of protein folds using multiple kernels where multiple heterogeneous feature space data are available. The proposed method provides the higher recognition ratio as compared to other methods reported in previous studies.

U2 - 10.1007/978-3-642-33065-0_31

DO - 10.1007/978-3-642-33065-0_31

M3 - Conference contribution

SN - 9783642330643

VL - 7440

SP - 292

EP - 299

BT - International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP)

A2 - Xiang, Yang

A2 - Stojmenovic, Ivan

A2 - Apduhan, Bernady O

A2 - Wang, Guojun

A2 - Nakano, Koji

A2 - Zomaya, Albert

PB - Springer

CY - Berlin Heidelberg

T2 - 12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012

Y2 - 4 September 2012 through 7 September 2012

ER -

Singh L, Chetty G , Sharma D. A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels. In Xiang Y, Stojmenovic I, Apduhan BO, Wang G, Nakano K, Zomaya A, editors, International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP): Lecture Notes in Computer Science. Vol. 7440. Berlin Heidelberg: Springer. 2012. p. 292-299 doi: 10.1007/978-3-642-33065-0_31

A novel approach to protein structure prediction using PCA based extreme learning machines and Multiple Kernels

Abstract

Conference

Access to Document

Fingerprint

Cite this