The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method.
Particularly, on the Cosmic2plus dataset, DX-SVMLight can identify 940 driver mutations, DX-LibSVM can identify 963 driver mutations, mRMR-RF can predict 902 driver mutations, and DX-RF predicts 892, but DX-RF method gets higher precision than DX-LibSVM, (59.91% versus 51.83%) and almost the same as DX-SVMLight.
The a priori based level set methods perform better than edge-based level set method; especially our method gets highest accuracy and makes less false segmentation.
None of these methods gets higher average accuracy than that of the proposed method, which is 96.28%.
In general, the least square and gradient descent [28] can be used to minimize Eq. (6), but the gradient descent method can get higher precision in the shortest time.
The comparison of EME values in Table 5 shows that the Global HE, BBHE, DSIHE, MMBEBHE, and WTHE methods usually get higher EME values than the remaining methods.
On the independent set of missense mutations, the DX-RF got higher 89.28%, 87.18%, and 85.53% accuracy than the other methods on the TP53, EGFR, and Cosmic2plus, respectively.
Figure 3 demonstrates that for most of the cases the proposed method gets much higher both recall and precision values in comparison with those of MetaCluster 5.0 and AbundanceBin.
The price for high-throughput genome sequencing has come down to $3,000 to $4,000 recently, and there are methods that are in development to use this inexpensive method to get high resolution brain connectivity information.
The price for high-throughput genome sequencing has come down to $3,000 £1,8355) to $4,000£2,45050) recently, and there are methods that are in development to use this inexpensive method to get high resolution brain connectivity information.
