Protein Folding Recognition

Lavneet Singh, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookChapter

Abstract

Protein folding recognition is a complex problem in bioinformatics where different structures of proteins are extracted from a large amount of harvested data including functional and genetic features of proteins. The data generated consist of thousands of feature vectors with fewer protein sequences. In such a case, we need computational tools to analyze and extract useful information from the vast amount of raw data to predict the major biological functions of genes and proteins with respect to their structural behavior. In this chapter, we discuss the predictability of protein folds using a new hybrid approach for selecting features and classifying protein data using support vector machine (SVM) classifiers with quadratic discriminant analysis (QDA) and principal component analysis (PCA) as generative classifiers to enhance the performance and accuracy. In one of the applied methods, we reduced the data dimensionality by using data reduction algorithms such as PCA. We compare our results with previous results cited in the literature and show that use of an appropriate feature selection technique is promising and can result in a higher recognition ratio compared with other competing methods proposed in previous studies. However, new approaches are still needed, as the problem is complex and the results are far from satisfactory. After this introductory section, the chapter is organized as follows: In Sect. 17.1 we discuss the problem of protein fold prediction, protein database, and its extracted feature vectors. Section 17.2 describes feature selection and classification using SVM and fused hybrid classifiers, while Sect. 17.4 presents the experimental results. Section 17.5 discusses experimental results, including conclusions and future work.
Original languageEnglish
Title of host publicationSpringer Handbook of Bio-/Neuro-Informatics
EditorsNikola Kasabov
Place of PublicationBerlin, Germany
PublisherSpringer
Chapter17
Pages265-273
Number of pages9
Edition1
ISBN (Electronic)9783642305740
ISBN (Print)9783642305733
DOIs
Publication statusPublished - 2014

Publication series

NameSpringer Handbooks
PublisherSpringer
ISSN (Print)2522-8692
ISSN (Electronic)2522-8706

Fingerprint

Protein folding
Proteins
Classifiers
Principal component analysis
Support vector machines
Feature extraction
Discriminant analysis
Bioinformatics
Data reduction
Genes

Cite this

Singh, L., & CHETTY, G. (2014). Protein Folding Recognition. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuro-Informatics (1 ed., pp. 265-273). (Springer Handbooks). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-30574-0_17
Singh, Lavneet ; CHETTY, Girija. / Protein Folding Recognition. Springer Handbook of Bio-/Neuro-Informatics. editor / Nikola Kasabov. 1. ed. Berlin, Germany : Springer, 2014. pp. 265-273 (Springer Handbooks).
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Singh, L & CHETTY, G 2014, Protein Folding Recognition. in N Kasabov (ed.), Springer Handbook of Bio-/Neuro-Informatics. 1 edn, Springer Handbooks, Springer, Berlin, Germany, pp. 265-273. https://doi.org/10.1007/978-3-642-30574-0_17

Protein Folding Recognition. / Singh, Lavneet; CHETTY, Girija.

Springer Handbook of Bio-/Neuro-Informatics. ed. / Nikola Kasabov. 1. ed. Berlin, Germany : Springer, 2014. p. 265-273 (Springer Handbooks).

Research output: A Conference proceeding or a Chapter in BookChapter

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Singh L, CHETTY G. Protein Folding Recognition. In Kasabov N, editor, Springer Handbook of Bio-/Neuro-Informatics. 1 ed. Berlin, Germany: Springer. 2014. p. 265-273. (Springer Handbooks). https://doi.org/10.1007/978-3-642-30574-0_17