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article · BMC Bioinformatics · 2008

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

Lukasz Kurgan, Krzysztof J. Cios, Ke Chen

Cited by 151Open access

Abstract

The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.

Cite this paper

APA 7

Kurgan, L., Cios, K. J., & Chen, K. (2008). SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences. BMC Bioinformatics, 9(1), 226. https://doi.org/10.1186/1471-2105-9-226

MLA 9

Kurgan, Lukasz, et al. “SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.” BMC Bioinformatics, vol. 9, no. 1, 2008, p. 226, https://doi.org/10.1186/1471-2105-9-226.

Chicago

Kurgan, Lukasz, Krzysztof J. Cios, and Ke Chen. 2008. “SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.” BMC Bioinformatics 9 (1): 226. https://doi.org/10.1186/1471-2105-9-226.

Harvard

Kurgan, L., Cios, K.J. and Chen, K. (2008) ‘SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences’, BMC Bioinformatics, 9(1), p. 226. Available at: https://doi.org/10.1186/1471-2105-9-226.

BibTeX
@article{kurgan2008scpred,
  title = {{SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences}},
  author = {Kurgan, Lukasz and Cios, Krzysztof J. and Chen, Ke},
  journal = {BMC Bioinformatics},
  volume = {9},
  number = {1},
  pages = {226},
  year = {2008},
  doi = {10.1186/1471-2105-9-226}
}

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