## 6 Predicting a Well-Known L-Type Pseudoknot  
In order to validate the efficiency of such platforms, it is common practice to construct a suitable dataset and compare the predicted secondary structures using specific metrics against the ground truth structure observed by biologists as well as other well-known platform predictions. Unfortunately, in the case of L-type pseudoknots, the dataset available is extremely limited. To date, only one L-type pseudoknot has been observed, which was initially presented in [52]. This RNA sequence was used as the input to the proposed platform as well as three other state-of-the-art methods: IPknot [26], ProbKnot [25], and Knotty [23]. The resulting dot-bracket notations are presented in Table 3, demonstrating that our platform, referred to as \"Knotify\", accurately predicted the core stems of the L-type pseudoknot. In contrast, Knotty and IPknot predicted H-type pseudoknots, which possess hairpins in the pseudoknot loops, while ProbKnot predicted a structure containing only hairpins, thus failing to predict the core stems of the L-type pseudoknot. It can be readily comprehended from Figure 3, which presents the ground truth in Subfigure a, our platform's prediction in Subfigure b, and Knotty, IPknot, and ProbKnotpredictions in Figure 3c,d,e, respectively, that the proposed system's prediction of an L-type pseudoknot closely approximates the actual pseudoknot structure. The process of visualizing the RNA pseudoknot structures shown in Figure 3 was executed through the utilization of the pseudoviewer tool [53]. In addition, we compared our platform's performance to other state-of-the-art methods, i.e., IPknot, ProbKnot, and Knotty, using the following four fundamental metrics. Table 4 displays the results for each method. * Positive predictive value (PPV): PPV is a metric that measures the proportion of true positives among the positive predictions made by a classification model. It is calculated as the ratio of true positives (TPs) to the sum of true positives and false positives (FPs): \(PPV=TP/(TP+FP)\)
* Recall: Recall is a metric that measures the proportion of true positives that were correctly identified by a classification model. It is calculated as the ratio of TPs to the sum of true positives and false negatives (FNs): \(Recall=TP/(TP+FN)\)
* F1-score: The F1-score is a metric that balances precision (PPV) and recall. It is calculated as the harmonic mean of precision and recall: \(F1\)-\(score=2*(PPV*Recall)/(PPV+Recall)\)
* Matthews correlation coefficient (MCC): MCC is a metric that takes into account all four outcomes of a binary classification model (true positives, false positives, true negatives, and false negatives). It is calculated as follows: \(MCC=(TP*TN-FP*FN)/\sqrt{((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))}\)  
Figure 3: Ground truth (**a**), our platform’s prediction (**b**), Knotty’s prediction (**c**), IPknot’s prediction (**d**), and ProbKnot’s prediction (**e**) of L-type pseudoknot presented in [52]. In order to assess the overall performance of our methodology, we focused on precision; recall; Matthews correlation coefficient (MCC); and F1-score, which is the harmonic mean of precision and recall. Our methodology surpassed the other two methods in terms of precision, with a score of 0.844, while Knotty had a score of 0.500, IPknot a score of 0.333, and ProbKnot a score of 0.308. In terms of the F1-score, our platform achieved a score of 0.675, while Knotty achieved a score of 0.661, which is very close to our platform, and both other platforms scored 0.449. The proposed methodology outperformed all methods in terms of the MCC metric, with a score of 0.521. Finally, Knotty achieved a very high recall score (0.976) by having only one false negative, which may have been due to the fact that large RNA sequences often contain multiple structures (such as bulges) that do not necessarily relate to the pseudoknot directly. This may have increased the overall true positive score and reduced the false negative score.