# A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping
## 2 Methodology of Research
This article revealed splitting sites for predicting eukaryotic exon exons using LSTM and RNN. The bidirectional LSTM structure enables networks to maintain both backward and forward data at each step, preserving sequence data in each hidden state. In Figure 1, the hidden layer has directed arrows displaying both backward and forward information. Itis feasible to preserve information from the past and future via disguised states [23]. The RNN model is more accurate because of the GRU's unique characteristic. Nevertheless, when applied to the current circumstance, our findings show that such a one-hot encoding technique often demonstrates limited generalization performance due to the generative model sparsity. To avoid the limitation, our approach encodes each nucleotide as a four-dimensional dense vector, the elements of which are chosen during training using the gradient descent method. As a result, the input layer of our RNN-based junction prediction approach is made up of four units. The input layer is linked to stacked RNN layers to simulate DNA sequences. Each hidden layer may include any RNN unit, and in our approach, we examine three distinct types: the rectified linear unit (ReLU) [24], the LSTM unit [25], and the GRU unit [26]. We use the LSTM unit in its whole, which includes the gates and peephole links. The top RNN layer's outputs are fed into a fully connected output layer with K units to forecast K-class junctions (i.e., K = 3 for accepting/donor/non-site classification and K = 2 for site/non-site classification). Activating the output completely connected layer, the sigmoid function is employed. Adam [27], a recently created optimizer, is used in our approach to optimize the multi-class logarithmic loss function for training. Adam consistently beat RMSprop [28], another novel optimizer, and several standard optimizers in our tests. We utilize RNN [29] to start the recurrent weight matrix with the identity matrix or a scaled version when using ReLUs in the RNN layer. The dropout approach is used to achieve regularization. The following RNN designs were tested: LSTM-based (4-70-40-3), GRU-based (4-70-40-3), and RNN-based (4-70-3), wherein the first and last numbers represent the input and output layers, respectively, while the middle numbers represent the number of units in the hidden RNN layers. The directed arrows in Figure 1 reflect both backward and forward information flows; w0, w1, w2, and wn represent the input, while y0, y1, y2, and yn represent the output, respectively. We are capable of working with DNA sequences or the whole genome. Thus,  
Figure 1: The architecture of Bidirectional Long short-term memory. as part of the dataset preparation technique, Open Reading Frames (ORFs) are predicted to accelerate the prediction process. Because DL models read input numerically, these ORFs are transformed into a categorical numerical representation. This work is complete after the data and model have been completed. The LSTM-RNN and GRU model is used to transmit the input data bidirectionally. By studying the DNA sequence, one may determine the position of the splice site donor and acceptor regions. Materials and processes contain an abundance of knowledge. Exon sequence prediction becomes more straightforward as our knowledge grows. Individuals who work with time-series data may use LSTM networks to classify, analyze, and forecast since there may be infinite delays between significant events in a time series that are not always visible. LSTMs were developed to address the problem of vanishing gradients that may occur when ordinary RNNs are trained [30]. While RNNs, hidden Markov models, and other algorithms for sequence learning must include gaps in the sequence, LSTMs need not. Introns are typically composed of two different nucleotides at either end, enabling them to function. The splicing site is where these nucleotides are situated. Because genes are not just a random collection of nucleotides but rather include unique properties, they may be identified via sequence analysis.