Feature extraction using clustering of protein

Isis Bonet; Yvan Saeys; Ricardo Grau Ábalo; María M. García; Robersy Sanchez; Yves Van De Peer

doi:10.1007/11892755_64

Feature extraction using clustering of protein

Isis Bonet, Yvan Saeys, Ricardo Grau Ábalo, María M. García, Robersy Sanchez, Yves Van De Peer

Biology

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

6 Scopus citations

Abstract

In this paper we investigate the usage of a clustering algorithm as a feature extraction technique to find new features to represent the protein sequence. In particular, our work focuses on the prediction of HIV protease resistance to drugs. We use a biologically motivated similarity function based on the contact energy of the amino acid and the position in the sequence. The performance measure was computed taking into account the clustering reliability and the classification validity. An SVM using 10-fold crossvalidation and the k-means algorithm were used for classification and clustering respectively. The best results were obtained by reducing an initial set of 99 features to a lower dimensional feature set of 36-66 features.

Original language	English (US)
Title of host publication	Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings
Publisher	Springer Verlag
Pages	614-623
Number of pages	10
ISBN (Print)	3540465561, 9783540465560
DOIs	https://doi.org/10.1007/11892755_64
State	Published - 2006
Event	11th Iberoamerican Congress in Pattern Recognition, CIARP 2006 - Cancun, Mexico Duration: Nov 14 2006 → Nov 17 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4225 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th Iberoamerican Congress in Pattern Recognition, CIARP 2006
Country/Territory	Mexico
City	Cancun
Period	11/14/06 → 11/17/06

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/11892755_64

Cite this

Bonet, I., Saeys, Y., Ábalo, R. G., García, M. M., Sanchez, R., & Van De Peer, Y. (2006). Feature extraction using clustering of protein. In Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings (pp. 614-623). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4225 LNCS). Springer Verlag. https://doi.org/10.1007/11892755_64

Bonet, Isis ; Saeys, Yvan ; Ábalo, Ricardo Grau et al. / Feature extraction using clustering of protein. Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings. Springer Verlag, 2006. pp. 614-623 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper we investigate the usage of a clustering algorithm as a feature extraction technique to find new features to represent the protein sequence. In particular, our work focuses on the prediction of HIV protease resistance to drugs. We use a biologically motivated similarity function based on the contact energy of the amino acid and the position in the sequence. The performance measure was computed taking into account the clustering reliability and the classification validity. An SVM using 10-fold crossvalidation and the k-means algorithm were used for classification and clustering respectively. The best results were obtained by reducing an initial set of 99 features to a lower dimensional feature set of 36-66 features.",

author = "Isis Bonet and Yvan Saeys and {\'A}balo, {Ricardo Grau} and Garc{\'i}a, {Mar{\'i}a M.} and Robersy Sanchez and {Van De Peer}, Yves",

year = "2006",

doi = "10.1007/11892755_64",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Bonet, I, Saeys, Y, Ábalo, RG, García, MM, Sanchez, R & Van De Peer, Y 2006, Feature extraction using clustering of protein. in Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4225 LNCS, Springer Verlag, pp. 614-623, 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Cancun, Mexico, 11/14/06. https://doi.org/10.1007/11892755_64

Feature extraction using clustering of protein. / Bonet, Isis; Saeys, Yvan; Ábalo, Ricardo Grau et al.
Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings. Springer Verlag, 2006. p. 614-623 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4225 LNCS).

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

TY - GEN

T1 - Feature extraction using clustering of protein

AU - Bonet, Isis

AU - Saeys, Yvan

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AU - Sanchez, Robersy

AU - Van De Peer, Yves

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AB - In this paper we investigate the usage of a clustering algorithm as a feature extraction technique to find new features to represent the protein sequence. In particular, our work focuses on the prediction of HIV protease resistance to drugs. We use a biologically motivated similarity function based on the contact energy of the amino acid and the position in the sequence. The performance measure was computed taking into account the clustering reliability and the classification validity. An SVM using 10-fold crossvalidation and the k-means algorithm were used for classification and clustering respectively. The best results were obtained by reducing an initial set of 99 features to a lower dimensional feature set of 36-66 features.

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M3 - Conference contribution

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SN - 9783540465560

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings

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Bonet I, Saeys Y, Ábalo RG, García MM, Sanchez R, Van De Peer Y. Feature extraction using clustering of protein. In Progress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings. Springer Verlag. 2006. p. 614-623. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11892755_64

Feature extraction using clustering of protein

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