ArticleGene: X2020
Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.
Article in Gene: X, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 58 citations in OpenAlex.
- A hybrid approach of ensemble learning and grey wolf optimizer for DNA splice junction prediction.PloS one · 2024Article
- A review of deep learning applications in human genomics using next-generation sequencing data.Human genomics · 2022Review
- From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.Human genomics · 2022Review
- Principles and correction of 5'-splice site selection.RNA biology · 2022Review
- Splice Junction Identification using Long Short-Term Memory Neural Networks.Current genomics · 2021Article
- How artificial intelligence might disrupt diagnostics in hematology in the near future.Oncogene · 2021Review
- DASSI: differential architecture search for splice identification from DNA sequences.BioData mining · 2021Article
Corrections and comments
- Reprinted as
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe accurate identification of the exon/intron boundaries is critical for the correct annotation of genes with multiple exons. Donor and acceptor splice sites (SS) demarcate these boundaries. Therefore, deriving accurate computational models to predict the SS are useful for functional annotation of genes and genomes, and for finding alternative SS associated with different diseases. Although various models have been proposed for the in silico prediction of SS, improving their accuracy is required for reliable annotation. Moreover, models are often derived and tested using the same genome, providing no evidence of broad application, i.e. to other poorly studied genomes.
resultsWith this in mind, we developed the Splice2Deep models for SS detection. Each model is an ensemble of deep convolutional neural networks. We evaluated the performance of the models based on the ability to detect SS in
conclusionsThe results of our study demonstrated that Splice2Deep both achieved a considerably reduced error rate compared to other state-of-the-art models and the ability to accurately recognize SS in other organisms for which the model was not trained, enabling annotation of poorly studied or newly sequenced genomes. Splice2Deep models are implemented in Python using Keras API; the models and the data are available at https://github.com/SomayahAlbaradei/Splice_Deep.git.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.