ArticleHuman mutation2019
Predicting the impact of single nucleotide variants on splicing via sequence-based deep neural networks and genomic features.
Article in Human mutation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- An Initial Indonesian Genome-Wide SNP-Array Study with Functional Variant Prioritization Reveals NASP and GPR78 Candidate SNVs in Hepatocellular Carcinoma.Biomedicines · 2026Article
- A KAN-based hybrid deep neural networks for accurate identification of transcription factor binding sites.PloS one · 2025Article
- RNA variant assessment using transactivation and transdifferentiation.American journal of human genetics · 2024Article
- Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network.Scientific reports · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- Computational prediction of human deep intronic variation.GigaScience · 2022Review
- A Novel Synonymous Variant of PHEX in a Patient with X-Linked Hypophosphatemia.Calcified tissue international · 2022Article
- Interpretable prioritization of splice variants in diagnostic next-generation sequencing.American journal of human genetics · 2021Article
- Benchmarking deep learning splice prediction tools using functional splice assays.Human mutation · 2021Article
- A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes.Nature communications · 2021Article
- Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation.Human mutation · 2019Article
- An effective deep learning-based approach for splice site identification in gene expression.Science progressArticle
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Single nucleotide mutations in exonic regions can significantly affect gene function through a disruption of splicing, and various computational methods have been developed to predict the splicing-related effects of a single nucleotide mutation. We implemented a new method using ensemble learning that combines two types of predictive models: (a) base sequence-based deep neural networks (DNNs) and (b) machine learning models based on genomic attributes. This method was applied to the Massively Parallel Splicing Assay challenge of the Fifth Critical Assessment of Genome Interpretation, in which challenge participants predicted various experimentally-defined exonic splicing mutations, and achieved a promising result. We successfully revealed that combining different predictive models based upon the stacked generalization method led to significant improvement in prediction performance. In addition, whereas most of the genomic features adopted in constructing machine learning models were previously reported, feature values generated with DSSP, a DNN-based splice site prediction tool, were novel and helpful for the prediction. Learning the sequence patterns associated with normal splicing and the change in splicing site probabilities caused by a mutation was presumed to be helpful in predicting splicing disruption.
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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.