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 γ 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 th

SUBMITTER: Yu X 

PROVIDER: S-EPMC4057401 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature