## 3 Results  
The target characteristic to be quantified at the completion of training was a mean distance between the learned filter cascades and the fixed, knowledge-representing cascades.  
We were interested in learning whether statistically significant differences exist among results produced for four different experimental scenarios:  
1. An unknown pattern present and filter similarity discouragement turned on;
2. An unknown pattern not present and filter similarity discouragement turned on;
3. An unknown pattern present and no filter similarity discouragement;
4. An unknown pattern not present and no filter similarity discouragement.  
For each scenario (repeated 100 times), the resulting mean distances between pairs of regular expressions represented by LCM filters and FCM filters were evaluated. Results of the [PERSON] (2005) and [PERSON] (2016) tests (widely accepted for equality of variance testing ([PERSON] et al., 2017))--for which the null hypothesis is that samples are drawn from the same distribution--show significant outcomes when group 2 is tested against group 4. For groups 1 and 3, test results give no basis for rejecting the null hypothesis; in both training regimes, rules that are similarly distant from the preset ones are learned (Table 2). In other words, statistical tests have significant results when the dataset does not contain unknown patterns. If the test result is insignificant, we can conclude that there is a new, unknown pattern in the data. The flowchart of this method is presented in Figure 5. It can also be observed that patterns learned by the trainable filter cascade are most similar to those actually present in the data (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Levene Test**} & \multicolumn{2}{c}{**Fligner Test**} \\ \(\lozenge\) & **Statistics** & **\(\boldsymbol{p}\)-Value** & **Statistics** & **\(\boldsymbol{p}\)-Value** \\ \hline True & 1.89 & 0.09 & 3.74 & 0.24 \\ False & 3.56 & 0.02 & 9.57 & 0.01 \\ \hline \hline \end{tabular} \(\lozenge\) Unknown regex present in data.  
\end{table}
Table 2: Results of statistical tests for GRCh38 data. Statistically significant results support the conclusion that samples are drawn from different distributions.  
Figure 5: The flowchart of the proposed testing method. Datasets with samples containing two known regexes are fed into two sets of networks of identical architecture; however, one has backprop modification turned on and the other has this mechanism turned off. After training, both scenarios’ distances between FCM and LCM are analyzed for variance equality. A lack of statistically significant differences indicates the presence of an unknown pattern C.  
The datasets used in our study were also analyzed using the two previously indicated baseline methods: DREME and MEME. The analysis was performed in two scenarios--for two or three patterns present in the data. Both methods were set to search for six-element-long motifs--the same as used in our method. Tables 4 and 5 show the results of experiments for DREME and MEME, respectively. As can be observed, both methods perform very well in discovering significant motifs; such a conclusion can be drawn by analysis of the E-value [14], which is responsible for motif significance. On the other hand, distances between the patterns discovered by these methods and the patterns considered in our experiments clearly show that neither of those two baselines can exploit prior knowledge, as the patterns they discover are unrelated to the target motifs. Moreover, both MEME and DREME admit very low pattern mutability, reporting multiple motifs that can be described with one regular expression using our methodology.