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

Research output: A Conference proceeding or a Chapter in BookConference contribution

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 languageEnglish
Title of host publicationInternational Conference on Algorithms and Architecture for Parallel Processing (ICA3PP)
Subtitle of host publicationLecture Notes in Computer Science
EditorsYang Xiang, Ivan Stojmenovic, Bernady O Apduhan, Guojun Wang, Koji Nakano, Albert Zomaya
Place of PublicationBerlin Heidelberg
PublisherSpringer
Pages292-299
Number of pages8
Volume7440
ISBN (Electronic)9783642330650
ISBN (Print)9783642330643
DOIs
Publication statusPublished - 2012
Event12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012: ICA3PP 2012 - Fukuoka, Fukuoka, Japan
Duration: 4 Sep 20127 Sep 2012
http://nsclab.org/ica3pp12/

Conference

Conference12th International Conference on Algorithms and Architectures for Parallel Processing ICA3PP 2012
Abbreviated titleICA3PP 2012
CountryJapan
CityFukuoka
Period4/09/127/09/12
OtherICA3PP 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

Fingerprint

Learning systems
Proteins
Bioinformatics
Learning algorithms
Support vector machines

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). Berlin Heidelberg: 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",

}

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 BookConference 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

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 https://doi.org/10.1007/978-3-642-33065-0_31