## 3 Related Work  
To the best of the authors' knowledge, this is the first attempt at a platform that incorporates a dedicated module to handle the prediction of RNA L-type pseudoknots. The proposed methodology aims to address this identified research gap. While there are several computational platforms available that can efficiently predict RNA pseudoknots, they mainly focus on H-type pseudoknots. Therefore, this section aims to present the proposed platforms in the literature, even if they are only capable of predicting H-type pseudoknots. Most RNA secondary structure prediction algorithms utilize dynamic programming as a core concept of their methodologies, attempting to calculate the structure with the lowest free energy. In the special case of pseudoknot prediction, in addition to the minimum free energy, the proposed frameworks also consider factors such as stability and entropy in their calculation pipeline, as in [18]. Despite the fact that this problem has been proven to be NP (nondeterministic polynomial time)-complete [19], researchers have developed stochastic and heuristic approaches [20; 21; 22]. Knotty [23], for example, is a highly efficient framework for pseudoknot prediction, which uses a CCJ (Chen-Condon-Jabbari) algorithm [24] with sparsification. ProbKnot [25] predicts the secondary structure of an RNA molecule by combining base pair probabilities of non-pseudoknotted structures and maximum expected accuracy. IPknot [26] and its extension [27] also take advantage of base pair probabilities, but they leverage integer programming and the LinearPartition model, combined with pseudo-expected accuracy, for further optimization in terms of accuracy. Various machine learning algorithms have also been proposed in the literature. These algorithms aim to identify underlined patterns in training datasets through supervised and unsupervised methods. Most of them rely on deep learning techniques, as in [28], where a deep learning method is employed with tertiary constraints, while in [29], a bidirectional LSTM network combined with IBPMP to select the correct base pairs and predict the optimal structure, was proposed. In 2 dRNA [30], a bidirectional LSTM encodes the data, which is then decoded by a fully connected network to produce the dot-bracket structure. The ATTfold method, capable of predicting pseudoknots [31], incorporates deep learning models with an attention mechanism as an encoder. The base pairing score matrix is encoded and then decoded by a convolutional neural network (CNN) into an appropriate format. UFold [32] is also a deep learning framework, and is trained directly on annotated data and base pairing rules. It utilizes an image-like representation of sequences, appropriate for processing by fully convolutional networks (FCNs). In addition to the thermodynamic models, there are also implementations using stochastic context-free grammar (SCFG). Their accuracy varies depending on the SCFG chosen. Pfold [33; 34], for example, receives RNA alignments as inputs and predicts a secondary structure. A multi-threaded version of Pfold, called PPfold [35], has also been developed for execution time optimization. RNA-Decoder [36] also predicts secondary structure using an SCFG, considering the known protein coding context of RNAs. Similar SCFG-based approaches include Contrafold [37], Evfold [38], Infernal [39], Oxford [40], and Stemloc [41]. Previous studies have demonstrated the possibility of translating [PERSON]'s thermodynamic model into an SCFG by calculating production probabilities from thermodynamic constants [22]. These techniques all strive to optimize an objective function, with thermodynamic methods endeavoring to minimize the free energy of a structure, deep learning approaches optimizing their loss function by adjusting their weights and trainable parameters, while SCFG-based methods aim to maximize the corresponding probability of a structure. The abundance of research on SCFG-based methods emphasizes the necessity for the effective amalgamation of all the above-mentioned methods.