Prediction of transcription factor families using DNA sequence features

Ashish Anand; Gary B. Fogel; Pugalenthi Ganesan; P. N. Suganthan

doi:10.1007/978-3-540-88436-1-14

Prediction of transcription factor families using DNA sequence features

Ashish Anand^*, Gary B. Fogel, Pugalenthi Ganesan, P. N. Suganthan

^*Corresponding author for this work

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

1 Scopus citations

Abstract

Understanding the mechanisms of protein-DNA interaction is of critical importance in biology. Transcription factor (TF) binding to a specific DNA sequence depends on at least two factors: A protein-level DNA-binding domain and a nucleotide-level specific sequence serving as a TF binding site. TFs have been classified into families based on these factors. TFs within each family bind to specific nucleotide sequences in a very similar fashion. Identification of the TF family that might bind at a particular nucleotide sequence requires a machine learning approach. Here we considered two sets of features based on DNA sequences and their physicochemical properties and applied a one-versus-all SVM (OVA-SVM) with class-wise optimized features to identify TF family-specific features in DNA sequences. Using this approach, a mean prediction accuracy of ~80% was achieved, which represents an improvement of ~7% over previous approaches on the same data.

Original language	English (US)
Title of host publication	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
Pages	154-164
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-540-88436-1-14
State	Published - Dec 8 2008
Event	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 - Melbourne, VIC, Australia Duration: Oct 15 2008 → Oct 17 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5265 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
Country/Territory	Australia
City	Melbourne, VIC
Period	10/15/08 → 10/17/08

Keywords

Multi-class classification
Transcription factor family prediction

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-88436-1-14

Cite this

Anand, A., Fogel, G. B., Ganesan, P., & Suganthan, P. N. (2008). Prediction of transcription factor families using DNA sequence features. In 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 (pp. 154-164). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5265 LNBI). https://doi.org/10.1007/978-3-540-88436-1-14

Anand, Ashish ; Fogel, Gary B. ; Ganesan, Pugalenthi et al. / Prediction of transcription factor families using DNA sequence features. 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. 2008. pp. 154-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Prediction of transcription factor families using DNA sequence features",

abstract = "Understanding the mechanisms of protein-DNA interaction is of critical importance in biology. Transcription factor (TF) binding to a specific DNA sequence depends on at least two factors: A protein-level DNA-binding domain and a nucleotide-level specific sequence serving as a TF binding site. TFs have been classified into families based on these factors. TFs within each family bind to specific nucleotide sequences in a very similar fashion. Identification of the TF family that might bind at a particular nucleotide sequence requires a machine learning approach. Here we considered two sets of features based on DNA sequences and their physicochemical properties and applied a one-versus-all SVM (OVA-SVM) with class-wise optimized features to identify TF family-specific features in DNA sequences. Using this approach, a mean prediction accuracy of ~80% was achieved, which represents an improvement of ~7% over previous approaches on the same data.",

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Anand, A, Fogel, GB, Ganesan, P & Suganthan, PN 2008, Prediction of transcription factor families using DNA sequence features. in 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5265 LNBI, pp. 154-164, 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008, Melbourne, VIC, Australia, 10/15/08. https://doi.org/10.1007/978-3-540-88436-1-14

Prediction of transcription factor families using DNA sequence features. / Anand, Ashish; Fogel, Gary B.; Ganesan, Pugalenthi et al.
3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. 2008. p. 154-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5265 LNBI).

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

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Anand A, Fogel GB, Ganesan P, Suganthan PN. Prediction of transcription factor families using DNA sequence features. In 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. 2008. p. 154-164. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-88436-1-14

Prediction of transcription factor families using DNA sequence features

Abstract

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Keywords

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