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Enhancing coding potential prediction for short sequences using complementary sequence features and feature selection

Yvan Saeys (UGent) and Yves Van de Peer (UGent)
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Abstract
The identification of coding potential in DNA sequences is of major importance in bioinformatics, where it is often used to assist expert systems that automatically try to recognize genes in genomes. For longer sequences, the identification of coding potential tends to be easier due to a better signal-to-noise ratio, whereas for very short sequences the issue becomes more problematic. In this paper, we present new methods that specifically aim at a better prediction of coding potential in short sequences. To this end, we combine different, complementary sequence features together with a feature selection strategy. Results comparing the new classifiers to state of the art models show that our new approach significantly outperforms the existing methods when applied to short sequences.
Keywords
GENES, ALGORITHMS

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Chicago
Saeys, Yvan, and Yves Van de Peer. 2007. “Enhancing Coding Potential Prediction for Short Sequences Using Complementary Sequence Features and Feature Selection.” In Lecture Notes in Bioinformatics, ed. Karl Tuyts, Ronald Westra, Yvan Saeys, and Ann Nowé, 4366:107–118. Berlin, Germany: Springer.
APA
Saeys, Y., & Van de Peer, Y. (2007). Enhancing coding potential prediction for short sequences using complementary sequence features and feature selection. In K. Tuyts, R. Westra, Y. Saeys, & A. Nowé (Eds.), Lecture Notes in Bioinformatics (Vol. 4366, pp. 107–118). Presented at the 1st International workshop on Knowledge Discovery and Emergent Complexity in Bioinformatics (KDECB 2006), Berlin, Germany: Springer.
Vancouver
1.
Saeys Y, Van de Peer Y. Enhancing coding potential prediction for short sequences using complementary sequence features and feature selection. In: Tuyts K, Westra R, Saeys Y, Nowé A, editors. Lecture Notes in Bioinformatics. Berlin, Germany: Springer; 2007. p. 107–18.
MLA
Saeys, Yvan, and Yves Van de Peer. “Enhancing Coding Potential Prediction for Short Sequences Using Complementary Sequence Features and Feature Selection.” Lecture Notes in Bioinformatics. Ed. Karl Tuyts et al. Vol. 4366. Berlin, Germany: Springer, 2007. 107–118. Print.
@inproceedings{750076,
  abstract     = {The identification of coding potential in DNA sequences is of major importance in bioinformatics, where it is often used to assist expert systems that automatically try to recognize genes in genomes. For longer sequences, the identification of coding potential tends to be easier due to a better signal-to-noise ratio, whereas for very short sequences the issue becomes more problematic. In this paper, we present new methods that specifically aim at a better prediction of coding potential in short sequences. To this end, we combine different, complementary sequence features together with a feature selection strategy. Results comparing the new classifiers to state of the art models show that our new approach significantly outperforms the existing methods when applied to short sequences.},
  author       = {Saeys, Yvan and Van de Peer, Yves},
  booktitle    = {Lecture Notes in Bioinformatics},
  editor       = {Tuyts, Karl and Westra, Ronald and Saeys, Yvan and Nowé, Ann},
  isbn         = {9783540710370},
  issn         = {0302-9743},
  keywords     = {GENES,ALGORITHMS},
  language     = {eng},
  location     = {Ghent, Belgium},
  pages        = {107--118},
  publisher    = {Springer},
  title        = {Enhancing coding potential prediction for short sequences using complementary sequence features and feature selection},
  url          = {http://dx.doi.org/10.1007/978-3-540-71037-0_7},
  volume       = {4366},
  year         = {2007},
}

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