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Title: EnsembleSplice: ensemble deep learning model for splice site prediction
Abstract Background

Identifying splice site regions is an important step in the genomic DNA sequencing pipelines of biomedical and pharmaceutical research. Within this research purview, efficient and accurate splice site detection is highly desirable, and a variety of computational models have been developed toward this end. Neural network architectures have recently been shown to outperform classical machine learning approaches for the task of splice site prediction. Despite these advances, there is still considerable potential for improvement, especially regarding model prediction accuracy, and error rate.

Results

Given these deficits, we propose EnsembleSplice, an ensemble learning architecture made up of four (4) distinct convolutional neural networks (CNN) model architecture combination that outperform existing splice site detection methods in the experimental evaluation metrics considered including the accuracies and error rates. We trained and tested a variety of ensembles made up of CNNs and DNNs using the five-fold cross-validation method to identify the model that performed the best across the evaluation and diversity metrics. As a result, we developed our diverse and highly effective splice site (SS) detection model, which we evaluated using two (2) genomicHomo sapiensdatasets and theArabidopsis thalianadataset. The results showed that for of theHomo sapiensEnsembleSplice achieved accuracies of 94.16% for one of the acceptor splice sites and 95.97% for donor splice sites, with an error rate for the sameHomo sapiensdataset, 4.03% for the donor splice sites and 5.84% for theacceptor splice sites datasets.

Conclusions

Our five-fold cross validation ensured the prediction accuracy of our models are consistent. For reproducibility, all the datasets used, models generated, and results in our work are publicly available in our GitHub repository here:https://github.com/OluwadareLab/EnsembleSplice

 
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NSF-PAR ID:
10372844
Author(s) / Creator(s):
; ;
Publisher / Repository:
Springer Science + Business Media
Date Published:
Journal Name:
BMC Bioinformatics
Volume:
23
Issue:
1
ISSN:
1471-2105
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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