# Classification of Precursor MicroRNAs from Different Species Based on K-mer Distance Features
## 7 Results and Discussion  
We have previously shown that k-mers can categorize miRNAs into species [27]. To improve the categorization, we devised three new sets of features also based on k-mers. The data used for categorization derives from clades with sufficient amounts of examples for classification (Table 1). The data represents various evolutionary distances (Figure 1). Previously, we have shown that it is possible to categorize species accurately at larger evolutionary distances, but categorizing becomes more difficult with decreasing evolutionary distance [26]. Therefore, it is important to have examples from varying evolutionary distances, in this case, also from various kingdoms (Figure 3). Each combination of species and clades needs a specific classifier, and these were trained using 100-fold MCCV. Initially, the known k-mer features were evaluated with respect to categorization accuracy (Figure 3). With one clade used for positive data and the other as negative data, 100 models were established using 80% training and 20% testing data. Classifier performance represents the average performance of 100-fold MCCV. These computations lead to a matrix where both columns and rows represent clades. Considering Aves versus Hexapoda as an example, the average accuracy amounts to 0.87 at 100-fold MCCV. This performance is 5% better than the general average of all established models. All the accuracy values on the diagonal of the table are the results of the clade with itself. Figure 3: Results in the figure stem from k-mer features only. Average accuracy for 100-fold Monte Carlo cross-validation using a random forest classifier and a split of 80% training and 20% testing. Yellow shades indicate lower accuracy, while red shades show higher average classification accuracy. This categorization is obviously very difficult, and accordingly, all accuracy values along the diagonal are very low. With the results in Figure 3, our previous observation regarding the effectiveness of k-mer features [26; 27], namely that the average model accuracy also increases with increasing evolutionary distance, can be confirmed. Although we have removed similar sequences between _Homo sapiens_ and its clade Hominidae, the performance is low (0.14 ACC), which indicates that some \"hidden message\" encoded in perhaps the triplet bias is conserved for the whole Hominidae clade. This observation is even more intriguing as the performance of Embryophyta versus its child clades is much better (0.70-0.78 ACC). Perhaps this is due to the larger evolutionary distance between Embryophyta and its child clades selected here, or the miRNAs may not be as strictly conserved within the plant kingdom. Considering the closest clades in the lower side of the phylogenetic tree (Figure 1), Cercopithecidae, Homo sapiens, and Rodentia, the performance is also the lowest (63-65 ACC; Table 1). Surprisingly, the performance of Brassicaceae and Malvaccae, which are also very closely related, is much better (74). K-mers have been introduced to parameterize pre-miRNAs early on [25], and many other sequence-based features have followed. Here, we did not use k-mers directly but transformed them to create new parameters do describe pre-miRNAs. Differentiation between pre-miRNAs from different species/clades was tested in the same manner as for k-mers (Figure 4). The inter k-mer distance was tested first (Figure 4). Like k-mers (Figure 3), roughly three hotspots with high accuracy for differentiation among species/clades can be observed. The most striking result is that virus pre-miRNAs seem to be very different from pre-miRNAs from the plant kingdom but much closer to pre-miRNAs from the metazoan clade. Since most viruses in the list infect animals, this may imply that their pre-miRNAs are functional in the host rather than for virus regulative activities, similar to what we had earlier proposed for _Toxoplasma gondii_[41; 42]. The other hotspots separate kingdoms from  
Figure 4: Results in the figure stem from k-mer distance features only.