Abstract
Selecting aptamers for human C-reactive protein (CRP) would be of critical importance in predicting the risk for cardiovascular disease. The enrichment level of DNA aptamers is an important parameter for selecting candidate aptamers for further affinity and specificity determination. This paper is the first report on pattern recognition used for CRP aptamer enrichment levels in the systematic evolution of ligands by exponential enrichment (SELEX) process, by applying structure-activity relationship models. After generating 10 rounds of graphene oxide (GO)-SELEX and 1670 molecular descriptors, eight molecular descriptors were selected and five latent variables were then obtained with principal component analysis (PCA), to develop a support vector classification (SVC) model. The SVC model (C=8.1728 and γ=0.2333) optimized by the particle swarm optimization (PSO) algorithm possesses an accuracy of 88.15% for the training set. Prediction results of enrichment levels for the sequences with the frequencies of 6 and 5 are reasonable and acceptable, with accuracies of 70.59% and 76.37%, respectively.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 21190041
Funding statement: This work has been supported by the Major Program of National Natural Science Foundation of China (Contract No. 21190041).
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©2017 Walter de Gruyter GmbH, Berlin/Boston
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- Frontmatter
- Review
- Bone plates for osteosynthesis – a systematic review of test methods and parameters for biomechanical testing
- Research articles
- Computer assisted evaluation of plate osteosynthesis of diaphyseal femur fracture considering interfragmentary movement: a finite element study
- Larger screw diameter may not guarantee greater pullout strength for headless screws – a biomechanical study
- Design considerations for patient-specific surgical templates for total hip arthroplasty with respect to acetabular cartilage
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