# A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping  
[PERSON] CANATALAY  
1 Faculty of Engineering, Computer Science, Altinbas University, 34217 Istanbul, Turkey; [EMAIL_ADDRESS]  
2  
[PERSON]  
1 Faculty of Engineering, Computer Science, Altinbas University, 34217 Istanbul, Turkey; [EMAIL_ADDRESS]  
Footnote 1: email: {[[https://www.mdpi.com/journal/applsci/](https://www.mdpi.com/journal/applsci/)]([https://www.mdpi.com/journal/applsci/](https://www.mdpi.com/journal/applsci/))  
Protein-coding sequences account for less than 5% of DNA in eukaryotes, remaining non-coding and untranslated [3; 4; 5]. The problem of identifying genes from DNA sequences is a significant one in bioinformatics. A coding sequence (CDS) is a segment of DNA that encodes for a particular protein [5]. The main distinctive aspect of a eukaryotic CDS structure is its division into exons and introns. A region known as the splice site divides these genes into exons and introns. There are four types of exons: \(5^{\prime}\) exons, internal exons, \(3^{\prime}\) exons, and exons missing an intron [6]. Introns are the non-coding portions of a gene, whereas exons are the protein-coding regions. Exon and intron sequences are frequently used to represent the sense strand of double-stranded DNA. Frequently, intron regions begin with G.T. bases and end with A.G. bases [7]. Splice sites are the regions of the genome that divide exons from introns; introns are deleted, and exons are reconnected during splicing. Splice site donors (GT) and acceptors (AG) are used to classifying introns [8]. The donor and acceptor of a splice site are employed as signals in exon prediction algorithms to locate splice sites. Precision in gene prediction is required to implement computational gene discovery from genomes. Machine learning and statistical approaches identify genes in anonymous DNA sequences. The Hidden Markov Model, Artificial Neural Networks, Genetic Networks, Decision Trees, and Statistical Approaches such as Bayes Theorem, Dimension Reduction, and Clustering are machine learning techniques. Machine learning is a subfield of artificial intelligence that constantly evolves and increases its utility in solving physical problems. Machine learning continuously improves a computer's algorithms to train it to learn [9; 10]. Machine learning is employed to its maximum potential in bioinformatics, such as genomics and predicting previously unknown factors in genome sequences. Due to the complexity of the eukaryotic genome, computational biologists have had problems predicting coding DNA regions. Machine learning is becoming increasingly important in bioinformatics as computational programming grows more sophisticated and prevalent. Deep learning (DL) is a subtype of machine learning involving learning from vast data using neural network algorithms [11]. One way to develop the DL model is to employ a multilayer artificial neural network. Each eukaryotic gene is composed of protein-coding and non-coding portions (introns). In the DNA sequence, the intron begins with a donor splice site region G.T. and ends with an acceptor splice site region AG [12]. The new precursor messenger RNA is spliced to convert a gene into a protein, removing introns and connecting them to exons. It is critical to identify donor splice sites accurately and acceptor splice sites in a genomic sequence for transcriptome research and protein diversity [13]. We applied both machine learning and DL techniques to resolve this issue. In machine learning and DL, respectively, Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN)- Gated recurrent unit (GRU) have been used. The fundamental premise of Artificial Neural Network (ANN) is that the human brain has many linked neurons or nerve cells [14]. According to artificial intelligence, the brain is a massively powerful, nonlinear, and parallel computer whose structural constituents or core units are referred to as neurons [15].