Prediction of tRNA Based on LS-SVM Algorithm with Principal Component Analysis

Chong Xing; Yao Wang; You Zhou; Yan Chun Liang

doi:10.4028/www.scientific.net/AMR.461.753

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 461Prediction of tRNA Based on LS-SVM Algorithm with...

Prediction of tRNA Based on LS-SVM Algorithm with Principal Component Analysis

Abstract:

Recently, non-coding RNA prediction is the one of the most important researches in bioinformatics. In this paper, on the basis of principal component analysis, we present a tRNA prediction strategy by using least squares support vector machine (LS-SVM). Appearance frequencies of single nucleotide, 2 – nucleotides and (G-C) %, (A-T) % were chosen as characteristics inputs. Results from tests showed that the prediction accuracy was 90.51% on prokaryotic tRNA dataset. Experimental results indicate that the method is effective for prokaryotic ncRNA prediction.

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Periodical:

Advanced Materials Research (Volume 461)

Pages:

753-756

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.461.753

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Online since:

February 2012

Authors:

Chong Xing, Yao Wang, You Zhou, Yan Chun Liang

Keywords:

Least Squares Support Vector Machines (Ls-SVM), Non-Coding RNA, Principal Component Analysis (PCA), Transfer RNA

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References

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