A Novel Approach to Protein Structure Prediction Using PCA or LDA Based Extreme Learning Machines

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

2 Citations (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 languageEnglish
Title of host publicationNeural Information Processing
EditorsTingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
Place of PublicationBerlin
PublisherSpringer
Pages1-6
Number of pages6
Volume7666
ISBN (Electronic)9783642344787
ISBN (Print)9783642344770
DOIs
Publication statusPublished - 2012
Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

Conference19th International Conference on Neural Information Processing 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

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  • Cite this

    Singh, L., Chetty, G., & Sharma, D. (2012). A Novel Approach to Protein Structure Prediction Using PCA or LDA Based Extreme Learning Machines. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), Neural Information Processing (Vol. 7666, pp. 1-6). Springer. https://doi.org/10.1007/978-3-642-34478-7_60