A novel approach to protein structure prediction using PCA or LDA based extreme learning machines

Authors:
Lavneet Singh;Girija Chetty;Dharmendra Sharma
Affiliations:
Faculty of Information Sciences & Engineering, University of Canberra, Australia;Faculty of Information Sciences & Engineering, University of Canberra, Australia;Faculty of Information Sciences & Engineering, University of Canberra, Australia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Year:
2012

Citing 10
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Ensemble classifier for protein fold pattern recognition

Bioinformatics
Sequence-based protein structure prediction using a reduced state-space hidden Markov model

Computers in Biology and Medicine
RotBoost: A technique for combining Rotation Forest and AdaBoost

Pattern Recognition Letters
Improving the protein fold recognition accuracy of a reduced state-space hidden Markov model

Computers in Biology and Medicine
Protein fold recognition with adaptive local hyperplane algorithm

CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Letters: Fully complex extreme learning machine

Neurocomputing
Letters: Ensemble of classifiers for protein fold recognition

Neurocomputing
A hybrid discriminative/generative approach to protein fold recognition

Neurocomputing
Real-time learning capability of neural networks

IEEE Transactions on Neural Networks

A comparative study of MRI data using various machine learning and pattern recognition algorithms to detect brain abnormalities

AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134

Quantified Score

Hi-index	0.00

Visualization

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.