Advanced tree-based kernels for protein classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

One of the aims of modern Bioinformatics is to discover the molecular mechanisms that rule the protein operation. This would allow us to understand the complex processes involved in living systems and possibly correct dysfunctions. The first step in this direction is the identification of the functional sites of proteins. In this paper, we propose new kernels for the automatic protein active site classification. In particular, we devise innovative attribute-value and tree substructure representations to model biological and spatial information of proteins in Support Vector Machines. We experimented with such models and the Protein Data Bank adequately pre-processed to make explicit the active site information. Our results show that structural kernels used in combination with polynomial kernels can be effectively applied to discriminate an active site from other regions of a protein. Such finding is very important since it firstly shows a successful identification of catalytic sites for a very large family of proteins belonging to a broad class of enzymes.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages218-229
Number of pages12
Volume4733 LNAI
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007 - Rome, Italy
Duration: 10 Sep 200713 Sep 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4733 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007
CountryItaly
CityRome
Period10/9/0713/9/07

Fingerprint

Protein Classification
kernel
Proteins
Protein
Catalytic Domain
Biological Models
Living Systems
Spatial Information
Bioinformatics
Substructure
Computational Biology
Support vector machines
Support Vector Machine
Enzymes
Attribute
Polynomials
Databases
Polynomial

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Cilia, E., & Moschitti, A. (2007). Advanced tree-based kernels for protein classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4733 LNAI, pp. 218-229). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4733 LNAI).

Advanced tree-based kernels for protein classification. / Cilia, Elisa; Moschitti, Alessandro.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4733 LNAI 2007. p. 218-229 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4733 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cilia, E & Moschitti, A 2007, Advanced tree-based kernels for protein classification. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4733 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4733 LNAI, pp. 218-229, 10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007, Rome, Italy, 10/9/07.
Cilia E, Moschitti A. Advanced tree-based kernels for protein classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4733 LNAI. 2007. p. 218-229. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Cilia, Elisa ; Moschitti, Alessandro. / Advanced tree-based kernels for protein classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4733 LNAI 2007. pp. 218-229 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{79bc560c7b1041068f1433f3bb03c8c5,
title = "Advanced tree-based kernels for protein classification",
abstract = "One of the aims of modern Bioinformatics is to discover the molecular mechanisms that rule the protein operation. This would allow us to understand the complex processes involved in living systems and possibly correct dysfunctions. The first step in this direction is the identification of the functional sites of proteins. In this paper, we propose new kernels for the automatic protein active site classification. In particular, we devise innovative attribute-value and tree substructure representations to model biological and spatial information of proteins in Support Vector Machines. We experimented with such models and the Protein Data Bank adequately pre-processed to make explicit the active site information. Our results show that structural kernels used in combination with polynomial kernels can be effectively applied to discriminate an active site from other regions of a protein. Such finding is very important since it firstly shows a successful identification of catalytic sites for a very large family of proteins belonging to a broad class of enzymes.",
author = "Elisa Cilia and Alessandro Moschitti",
year = "2007",
month = "12",
day = "1",
language = "English",
isbn = "9783540747819",
volume = "4733 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "218--229",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Advanced tree-based kernels for protein classification

AU - Cilia, Elisa

AU - Moschitti, Alessandro

PY - 2007/12/1

Y1 - 2007/12/1

N2 - One of the aims of modern Bioinformatics is to discover the molecular mechanisms that rule the protein operation. This would allow us to understand the complex processes involved in living systems and possibly correct dysfunctions. The first step in this direction is the identification of the functional sites of proteins. In this paper, we propose new kernels for the automatic protein active site classification. In particular, we devise innovative attribute-value and tree substructure representations to model biological and spatial information of proteins in Support Vector Machines. We experimented with such models and the Protein Data Bank adequately pre-processed to make explicit the active site information. Our results show that structural kernels used in combination with polynomial kernels can be effectively applied to discriminate an active site from other regions of a protein. Such finding is very important since it firstly shows a successful identification of catalytic sites for a very large family of proteins belonging to a broad class of enzymes.

AB - One of the aims of modern Bioinformatics is to discover the molecular mechanisms that rule the protein operation. This would allow us to understand the complex processes involved in living systems and possibly correct dysfunctions. The first step in this direction is the identification of the functional sites of proteins. In this paper, we propose new kernels for the automatic protein active site classification. In particular, we devise innovative attribute-value and tree substructure representations to model biological and spatial information of proteins in Support Vector Machines. We experimented with such models and the Protein Data Bank adequately pre-processed to make explicit the active site information. Our results show that structural kernels used in combination with polynomial kernels can be effectively applied to discriminate an active site from other regions of a protein. Such finding is very important since it firstly shows a successful identification of catalytic sites for a very large family of proteins belonging to a broad class of enzymes.

UR - http://www.scopus.com/inward/record.url?scp=38049112942&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38049112942&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:38049112942

SN - 9783540747819

VL - 4733 LNAI

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

SP - 218

EP - 229

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

ER -