[1] Weng Jianhong, Zhou Tong, Sun Xiao, Lu Zuhong, et al. Support vector machine for prediction of meiotic recombinationhotspots and coldspots in Saccharomyces cerevisiae [J]. Journal of Southeast University (English Edition), 2006, 22 (1): 112-116. [doi:10.3969/j.issn.1003-7985.2006.01.024]

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Support vector machine for prediction of meiotic recombinationhotspots and coldspots in Saccharomyces cerevisiae()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

22

Issue:

2006 1

Page:

112-116

Research Field:

Biological Science and Medical Engineering

Publishing date:

2006-03-20

Info

Title:

Support vector machine for prediction of meiotic recombinationhotspots and coldspots in Saccharomyces cerevisiae

Author(s):

Weng Jianhong;  Zhou Tong;  Sun Xiao;  Lu Zuhong

State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China

Keywords:

meiotic recombination;  hotspot;  coldspot;  dinucleotide abundance;  support vector machine

PACS:

Q617

DOI:

10.3969/j.issn.1003-7985.2006.01.024

Abstract:

A novel method for predicting hotspots and coldspots using support vector machine(SVM)based on statistical learning theory is developed. This method is applied to published 303 hot and 48 cold open reading frames(ORFs)in Saccharomyces cerevisiae. The sequence features of general dinucleotide abundance and dinucleotide abundance based on codon usage are extracted, and then the data sets are classified with different parameters and kernel functions combined with the method of two-fold cross validation. The result indicates that 87.47% accuracy can be reached when classifying hot and cold ORF sequences with the kernel of radial basis function combined with dinucleotide abundance based on codon usage.

References:

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Memo

Memo:

Biographies: Weng Jianhong(1981—), male, graduate;Lu Zuhong(corresponding author), male, doctor, professor, zhlu@seu.edu.cn.

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