Dataset Information

Support vector machine classification of streptavidin-binding aptamers.

ABSTRACT:

Background

Synthesizing and characterizing aptamers with high affinity and specificity have been extensively carried out for analytical and biomedical applications. Few publications can be found that describe structure-activity relationships (SARs) of candidate aptamer sequences.

Methodology

This paper reports pattern recognition with support vector machine (SVM) classification techniques for the identification of streptavidin-binding aptamers as "low" or "high" affinity aptamers. The SVM parameters C and ? were optimized using genetic algorithms. Four descriptors, the topological descriptor PW4 (path/walk 4--Randic shape index), the connectivity index X3A (average connectivity index chi-3), the topological charge index JGI2 (mean topological charge index of order 2), and the free energy E of the secondary structure, were used to describe the structures of candidate aptamer sequences from SELEX selection (Schütze et al. (2011) PLoS ONE (12):e29604).

Conclusions

The predicted fractions of winning streptavidin-binding aptamers for ten rounds of SELEX conform to the aptamer evolutionary principles of SELEX-based screening. The feasibility of applying pattern recognition based on SVM and genetic algorithms for streptavidin-binding aptamers has been demonstrated.

SUBMITTER: Yu X

PROVIDER: S-EPMC4057401 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Support vector machine classification of streptavidin-binding aptamers.

Yu Xinliang X Yu Yixiong Y Zeng Qun Q

PloS one 20140613 6

<h4>Background</h4>Synthesizing and characterizing aptamers with high affinity and specificity have been extensively carried out for analytical and biomedical applications. Few publications can be found that describe structure-activity relationships (SARs) of candidate aptamer sequences.<h4>Methodology</h4>This paper reports pattern recognition with support vector machine (SVM) classification techniques for the identification of streptavidin-binding aptamers as "low" or "high" affinity aptamers. ...[more]

PMID: 24927174

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Dataset Information

Support vector machine classification of streptavidin-binding aptamers.

Background

Methodology

Conclusions

Publications

Support vector machine classification of streptavidin-binding aptamers.

Similar Datasets

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