Cargando…
EnsembleSplice: ensemble deep learning model for splice site prediction
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 develope...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535948/ https://www.ncbi.nlm.nih.gov/pubmed/36203144 http://dx.doi.org/10.1186/s12859-022-04971-w |
_version_ | 1784802886506512384 |
---|---|
author | Akpokiro, Victor Martin, Trevor Oluwadare, Oluwatosin |
author_facet | Akpokiro, Victor Martin, Trevor Oluwadare, Oluwatosin |
author_sort | Akpokiro, Victor |
collection | PubMed |
description | 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) genomic Homo sapiens datasets and the Arabidopsis thaliana dataset. The results showed that for of the Homo sapiens EnsembleSplice 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 same Homo sapiens dataset, 4.03% for the donor splice sites and 5.84% for the acceptor 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 |
format | Online Article Text |
id | pubmed-9535948 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95359482022-10-07 EnsembleSplice: ensemble deep learning model for splice site prediction Akpokiro, Victor Martin, Trevor Oluwadare, Oluwatosin BMC Bioinformatics Research 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) genomic Homo sapiens datasets and the Arabidopsis thaliana dataset. The results showed that for of the Homo sapiens EnsembleSplice 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 same Homo sapiens dataset, 4.03% for the donor splice sites and 5.84% for the acceptor 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 BioMed Central 2022-10-06 /pmc/articles/PMC9535948/ /pubmed/36203144 http://dx.doi.org/10.1186/s12859-022-04971-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Akpokiro, Victor Martin, Trevor Oluwadare, Oluwatosin EnsembleSplice: ensemble deep learning model for splice site prediction |
title | EnsembleSplice: ensemble deep learning model for splice site prediction |
title_full | EnsembleSplice: ensemble deep learning model for splice site prediction |
title_fullStr | EnsembleSplice: ensemble deep learning model for splice site prediction |
title_full_unstemmed | EnsembleSplice: ensemble deep learning model for splice site prediction |
title_short | EnsembleSplice: ensemble deep learning model for splice site prediction |
title_sort | ensemblesplice: ensemble deep learning model for splice site prediction |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535948/ https://www.ncbi.nlm.nih.gov/pubmed/36203144 http://dx.doi.org/10.1186/s12859-022-04971-w |
work_keys_str_mv | AT akpokirovictor ensemblespliceensembledeeplearningmodelforsplicesiteprediction AT martintrevor ensemblespliceensembledeeplearningmodelforsplicesiteprediction AT oluwadareoluwatosin ensemblespliceensembledeeplearningmodelforsplicesiteprediction |