Results and discussion Results of optimization for DNA sensor model The parameters to be optimized in this model were A, B and C in Equation 2 which create a solution space of four dimensions with three variables and one
function known as fitness function. The best results obtained out of 20 runs are shown in Table 1 which introduce the lowest fitness values. PF-02341066 nmr Table 1 The best values of the optimizing parameters over the 20 runs The best fitness value obtained Optimized value for A Optimized value for B Optimized value for C 6.742e-07 2.138e10 8.9921e9 -5.680e3 The experimental waveform of the DNA sensor is used for obtaining the optimized values for parameters A, B and C. The optimized model and the experimental waveforms are shown in Figure 3. Figure 3 DNA sensor characteristics. The experimental selleck chemicals and optimized model waveforms for DNA sensor in the presence of probe DNA. The mean absolute percentage error (MAPE) index is used to assess the quality of the MM-102 modelled waveform (see Equation 7). (7) The optimized model is evaluated
using the MAPE index for different concentrations of the DNA sensor. Table 2 shows the accuracy of the proposed optimized model for six different concentrations of the DNA sensor covering a range from 0.01 to 500 nM. The lowest accuracy obtained is 98.46% for the concentration of 0.01 nM while the highest accuracy is 99.41% belonging to the concentration of 100 nM. Overall, the accuracy of more than 98% represents an overall error of less than 2% which is quite acceptable for the optimized model. Table 2 The Dichloromethane dehalogenase MAPE value for different concentrations of DNA sensor ( F ) Concentration F (nM) MAPE value (%) Accuracy based on MAPE (%) F = 0.01 1.54 98.46 F = 0.1
0.90 99.10 F = 1 1.03 98.97 F = 10 0.77 99.23 F = 100 0.59 99.41 F = 500 0.93 99.07 In the next section, it is demonstrated that the optimized model of solution-gated graphene-based DNA sensors can be utilized for electrical detection of DNA hybridization application. DNA hybridization detection using the optimized model The detection of DNA hybridization has been a topic of central importance owing to a wide variety of applications such as diagnosis of pathogenic and genetic disease, gene expression analysis and the genotyping of mutations and polymorphisms [46, 47]. Technologies in DNA biosensing  have received special appeal not only for their low cost and simplicity but also for their ultimate capabilities in detecting single-nucleotide polymorphisms (SNP) which have been correlated to several diseases and genetic disorders such as Alzheimer and Parkinson diseases. The DNA hybridization event is the basis of many existing DNA detection techniques. In DNA hybridization as depicted in Figure 4, the target, unknown single-stranded DNA (ssDNA), is identifid and formed by a probe ssDNA and a double-stranded (dsDNA) helix structure with two complementary strands.