We envision that this quantification method can be helpful in the case of rare disease-associated mutations, allowing clinicians to choose the appropriate dose of drug for optimal or maximal TTR stabilization, as clinical trials or even clinical studies may not be practical for these small populations.
Assessment of epidermal growth factor receptor (EGFR) mutations has become mandatory to choose the most active first-line treatment for patients with advanced non-small-cell lung cancer (NSCLC).
After the ENU treatment, a large F1 population is generated to choose the mutations for purpose from many random heterozygous mutations in their genomes.
Identifying the mutations can then be used to choose the best treatment.
However, in our recent study using GA as mentioned above, we considered the mutation of the genotype (network) to choose a higher fitness value given by the phenotype, to increase heterogeneity of expression patterns over cells.
This subsection addresses the issue of how to choose an appropriate mutation reference point for each solution within the individual to be mutated, since it is critical for the distribution of solutions in the individual after mutation.
Some women with these mutations choose to have surgery to reduce their cancer risk (Berns and Bowtell, 2012), and studying tissues from these patients has increased our understanding of how the disease progresses.
In our study, many mutations were missense mutations; therefore, we chose to address missense mutations.
For example, to associate a disease with its possible causal mutations, investigators often choose to retrospectively compare the nucleotide difference between the cases and the controls because the comparison can be accomplished quickly and economically.
