# A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping
## 2 Methodology of Research  
Constitutive splice sites are often distinguished from alternative splice sites. These, however, are functional rather than splice site properties. A constitutive splice site, for example, may be located close to a usually undetected cryptic splice site. However, a variant may activate the cryptic site, enabling the previously constitutive splice site to be used. As a result, two variables may impact splice site utilization: The inherent strength of the splice site, as measured by surrounding splicing enhancer-binding motifs, for example. However, utilization is also determined by the strength of nearby splice sites, which may be used alternately and compete for spliceosome recognition. Consequently, we propose that the competitive component of splice site selection be represented in addition to modeling the splice site itself. Another innovation in the field is using DL to learn features directly from sequences rather than generating them manually. Manually constructing feature sets may be very time-intensive, and calculating constraints might limit the size of the training dataset. Convolutional networks have been used successfully to learn transcription factor binding patterns, predict non-coding DNA function, and predict a wide range of epigenetic and transcriptional profiles. Recurrent neural networks with LSTM-RNN and GRU are a DL model which refers to a neural network approach with numerous layers. Typical feedforward neural networks have fixed-size input and output. These networks are not well-suited for processing sequences or time-series data. A recurrent neural network is a kind of neural network that may be used to extract data from sequences or data series. RNN is a feedforward neural network extension that includes loops in the hidden layers. The RNN is given a series of samples and charged with establishing a temporal relationship between them. LSTM approach overcomes classification challenges by combining network parameters with the hidden node and releasing state depending on the input values. RNN outperforms LSTM by activating states in response to network events. A typical RNN node has the same bias and weight. The RNN is evaluated using a gated recurrent unit and an LSTM. The network parameters define a one-to-one network architecture in which each input data time step is utilized to create an output with the same time step. It was built with that purpose in mind. DL models perform well with any length sequences and time-series data, and their inputs and outputs may be any size. They come in a range of sizes and outputs. RNN is an example of a machine that loops across many networks. Information may remain due to the looping networks. Each network in the loop gets data and information from the network preceding it, conducts the operation, and sends the data and information to the network before it [18]. The procedure of generating a multilayer RNN is simpler since the output of the first layer serves as the input for the second layer. This simplifies the process of developing a more accurate multilayer RNN. Certain people, but not all, seek knowledge to understand more about what transpired in the past. While learning new information, neural networks that extensively use common recurrent connections take longer to train than other neural networks. This has an impact on the model accuracy. The occurrences are amenable to learning using LSTM networks, a kind of RNN [19]. These networks are designed to avoid the problem of long-term reliance that recurrent neural networks have [20]. A few more interactions are introduced to the RNN using LSTM to boost its accuracy. LSTMs are used in DL [21]. The architecture is based on an RNN. In contrast to conventional feedforward neural networks, LSTMs have connections that allow data to be sent back. Individual data points and complete data sequences may be processed. There are two kinds of RNNs: LSTM and LSTM-RNN-GRU. The author of [22] suggested it.