Texture classification of proteins using support vector machines and bio-inspired metaheuristics

Carlos Fernandez-Lozano; Julian Dorado; Jose A. Seoane; Pablo Mesejo; Youssef S.G. Nashed; Stefano Cagnoni

doi:10.1007/978-3-662-44485-6_9

Texture classification of proteins using support vector machines and bio-inspired metaheuristics

Carlos Fernandez-Lozano^*, Julian Dorado, Jose A. Seoane, Pablo Mesejo, Youssef S.G. Nashed, Stefano Cagnoni

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process.

Original language	English (US)
Title of host publication	Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers
Editors	Ana Fred, Hugo Gamboa, Pedro L. Fernandes, Jordi Solé-Casals, Mireya Fernández-Chimeno, Sergio Alvarez, Deborah Stacey
Publisher	Springer Verlag
Pages	117-130
Number of pages	14
ISBN (Electronic)	9783662444849
DOIs	https://doi.org/10.1007/978-3-662-44485-6_9
State	Published - 2014
Event	6th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2013 - Barcelona, Spain Duration: Feb 11 2013 → Feb 14 2013

Publication series

Name	Communications in Computer and Information Science
Volume	452
ISSN (Print)	1865-0929

Other

Other	6th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2013
Country/Territory	Spain
City	Barcelona
Period	2/11/13 → 2/14/13

Funding

This work is by “Development of new image analysis techniques in 2D Gel for biomedical research” (Ref. 10SIN105004PR), CN2102/217, CN2011/034 and CN2012/130 by Xunta de Galicia. Jose A. Seoane acknowledges Medical Research Council Project Grant G1000427. Pablo Mesejo and Youssef S.G. Nashed are funded by the European Commission (MIBISOC Marie Curie Initial Training Network, FP7 PEOPLE-ITN-2008, GA n. 238819).

Keywords

Electrophoresis
Feature Selection
Genetic Algorithm
Proteomic Imaging
Support
Texture Analysis
Vector Machines

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-662-44485-6_9

Cite this

Fernandez-Lozano, C., Dorado, J., Seoane, J. A., Mesejo, P., Nashed, Y. S. G., & Cagnoni, S. (2014). Texture classification of proteins using support vector machines and bio-inspired metaheuristics. In A. Fred, H. Gamboa, P. L. Fernandes, J. Solé-Casals, M. Fernández-Chimeno, S. Alvarez, & D. Stacey (Eds.), Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers (pp. 117-130). (Communications in Computer and Information Science; Vol. 452). Springer Verlag. https://doi.org/10.1007/978-3-662-44485-6_9

Fernandez-Lozano, Carlos ; Dorado, Julian ; Seoane, Jose A. et al. / Texture classification of proteins using support vector machines and bio-inspired metaheuristics. Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers. editor / Ana Fred ; Hugo Gamboa ; Pedro L. Fernandes ; Jordi Solé-Casals ; Mireya Fernández-Chimeno ; Sergio Alvarez ; Deborah Stacey. Springer Verlag, 2014. pp. 117-130 (Communications in Computer and Information Science).

@inproceedings{fcc21b90e0134e6381f0cb4aadafdbe3,

title = "Texture classification of proteins using support vector machines and bio-inspired metaheuristics",

abstract = "In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process.",

keywords = "Electrophoresis, Feature Selection, Genetic Algorithm, Proteomic Imaging, Support, Texture Analysis, Vector Machines",

author = "Carlos Fernandez-Lozano and Julian Dorado and Seoane, {Jose A.} and Pablo Mesejo and Nashed, {Youssef S.G.} and Stefano Cagnoni",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2014.; 6th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2013 ; Conference date: 11-02-2013 Through 14-02-2013",

year = "2014",

doi = "10.1007/978-3-662-44485-6_9",

language = "English (US)",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "117--130",

editor = "Ana Fred and Hugo Gamboa and Fernandes, {Pedro L.} and Jordi Sol{\'e}-Casals and Mireya Fern{\'a}ndez-Chimeno and Sergio Alvarez and Deborah Stacey",

booktitle = "Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers",

}

Fernandez-Lozano, C, Dorado, J, Seoane, JA, Mesejo, P, Nashed, YSG & Cagnoni, S 2014, Texture classification of proteins using support vector machines and bio-inspired metaheuristics. in A Fred, H Gamboa, PL Fernandes, J Solé-Casals, M Fernández-Chimeno, S Alvarez & D Stacey (eds), Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers. Communications in Computer and Information Science, vol. 452, Springer Verlag, pp. 117-130, 6th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2013, Barcelona, Spain, 2/11/13. https://doi.org/10.1007/978-3-662-44485-6_9

Texture classification of proteins using support vector machines and bio-inspired metaheuristics. / Fernandez-Lozano, Carlos; Dorado, Julian; Seoane, Jose A. et al.
Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers. ed. / Ana Fred; Hugo Gamboa; Pedro L. Fernandes; Jordi Solé-Casals; Mireya Fernández-Chimeno; Sergio Alvarez; Deborah Stacey. Springer Verlag, 2014. p. 117-130 (Communications in Computer and Information Science; Vol. 452).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Texture classification of proteins using support vector machines and bio-inspired metaheuristics

AU - Fernandez-Lozano, Carlos

AU - Dorado, Julian

AU - Seoane, Jose A.

AU - Mesejo, Pablo

AU - Nashed, Youssef S.G.

AU - Cagnoni, Stefano

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2014.

PY - 2014

Y1 - 2014

N2 - In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process.

AB - In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process.

KW - Electrophoresis

KW - Feature Selection

KW - Genetic Algorithm

KW - Proteomic Imaging

KW - Support

KW - Texture Analysis

KW - Vector Machines

UR - http://www.scopus.com/inward/record.url?scp=84916205008&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84916205008&partnerID=8YFLogxK

U2 - 10.1007/978-3-662-44485-6_9

DO - 10.1007/978-3-662-44485-6_9

M3 - Conference contribution

AN - SCOPUS:84916205008

T3 - Communications in Computer and Information Science

SP - 117

EP - 130

BT - Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers

A2 - Fred, Ana

A2 - Gamboa, Hugo

A2 - Fernandes, Pedro L.

A2 - Solé-Casals, Jordi

A2 - Fernández-Chimeno, Mireya

A2 - Alvarez, Sergio

A2 - Stacey, Deborah

PB - Springer Verlag

T2 - 6th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2013

Y2 - 11 February 2013 through 14 February 2013

ER -

Fernandez-Lozano C, Dorado J, Seoane JA, Mesejo P, Nashed YSG, Cagnoni S. Texture classification of proteins using support vector machines and bio-inspired metaheuristics. In Fred A, Gamboa H, Fernandes PL, Solé-Casals J, Fernández-Chimeno M, Alvarez S, Stacey D, editors, Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers. Springer Verlag. 2014. p. 117-130. (Communications in Computer and Information Science). doi: 10.1007/978-3-662-44485-6_9

Texture classification of proteins using support vector machines and bio-inspired metaheuristics

Abstract

Publication series

Other

Funding

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this