Although the above approach is fast, it often performs poorly in predicting categories that have a small number of annotations (as discussed in Section 5).
The category with the maximum number of votes is taken as the predicted category.
The category with the highest number of segments was chosen as the predicted category.
The selection of the predicted category is then done through a plurality voting procedure.
A voting process is then carried out to choose the predicted category.
Boxplots for each predicted category were plotted separately for WT (grey) and mutated sequences (purple).
We assign an error score as follows which will be a positive integer; 0 if the predicted category is the same as the actual category, 1 if the predicted category is ±1 away from the actual category, etc.
From the predicted category, we could then calculate the classification accuracy for the ipsi- and contralateral fingers independently.
The probability and modeling approach, in contrast to an overlapping peak approach, allows variations in features within the predicted category.
The predicted Category 2 SNP at position 94 for MSSNPV075 was confirmed, and additional SNPs were identified at position 42 and position 48.
