# A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping
## 3 Discussion and Results
### Testing Results for the Bidirectional LSTM-RNN and GRU Model  
Twenty percent of the whole dataset was used to test the model [35]. Test x and Test Y account for 20% of the dataset. Twenty percent of the testing data is used to assess the model. Table 3 contains 20% of the test data and the x and Y vectors, including all nucleotide sequences and their window size and labels. Test data is used to test the model's ability to abstract from various sources of information. The model achieves a 96.1 percent accuracy on the test data. We tried 20:80 epochs with a cycle rate ranging from 10 to 70, and the best result was 50 cycles off 70 epochs. Before and after testing the model, a random whole genome sequence of _C. parvum_ was used as an input sequence to predict the exons from the trained model. To improve the accuracy of intron and exon prediction, a filter based on intron length was added to the model. This increased the model's precision. As follows the model's execution, it will be assigned the labels 0 and 1, and 2. This demonstrates that in a certain window of sequences, the counter indicates that 0 equals G.T., 1 equals A.G., and 2 equals no-site. This is true if a window has more than a specified number of counts for each label. It considers the intron length restriction and generates 681 introns with an average length of 89. Exons anticipated to have an average length of 1370 were predicted to have an average length of 1370. There were 4618 exons, each around 1370 words in length. Table 4 summarizes the suggested model's exon and intron predictions. These predictions are compared to the annotated genome of _C. parvum_[36]. The length of an annotated CDS sequence for _C. parvum_ ATCC mRNA. Due to the high gene density and small distances between genes, accurately delineating UTR boundaries using short-read sequencing data is challenging. Without assistance from the genome or reference annotation, RNA-Seq for transcriptome assembly results in a high rate of chimerism. As a result, we configured the assembly process using reference annotation and refined the parameters to decrease the amount of incorrectly fused transcripts. Due to the length limits imposed by the two fundamental splicing mechanisms, spliced exons prefer longer introns. Exons are DNA segments that contain the amino acid sequence of a gene. Most gene sequences are interrupted by one or more DNA regions termed introns in plants and animals. If an alternative exon was chosen, it was certainly included among other alternative exons discovered downstream. Thus, variants that contained one alternatively spliced exon skipped the next and then included others downstream were fairly in-frequent, accounting for 18, 14, 16, and 20% of alternatively spliced isoforms from the colon, respectively. Spliced exons are more often found in longer introns due to the length limits imposed by the two principal splicing processes for splice site pairing. Ambiguities in the gene models of all of these  
Figure 4: Loss curve and accuracy with the best epoch for the training model. parasites, including transcription, start and stop sites, translation start and stop sites, and identification of introns and intron-exon borders, must be addressed. Additionally, optimization of these genome assemblies, additional investigations to support genome annotations, and discovery and characterization of discrepancies across these genome assemblies are necessary. These genomes may help to explain variations in Cryptosporidium species traits, such as host range and pathogenicity. Our Improved Genome Annotation (IGA) revealed variations in the number of genes and intron content in _C. parvum's_ coding genome. The IGA identified 3865 protein-coding genes, including 74 that the OGA had overlooked [37]. Most genes lacked introns; nevertheless, whereas the OGA discovered introns in only 4.3 percent of genes, the IGA identified introns in 10.8 percent [38]. Additionally, in the revised annotation, fourteen of the OGA's coding regions were recognized as exons belonging to nearby genes.