ArticlePloS one2022
CI-SpliceAI-Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- NovelJournal of clinical research in pediatric endocrinology · 2026Article
- Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.Genome biology · 2026Article
- Integration of Machine Learning-Based Pathogenicity Prediction and Phenotype Matching Improves Variant Prioritization in Rare Clinical Testing.Current issues in molecular biology · 2026Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- Genetic and Molecular Characterization of Treacher Collins Syndrome in Three Mexican Families.International journal of molecular sciences · 2026Article
- Article
- Case Report: Functional validation of aFrontiers in genetics · 2026Article
- Whole Genome Sequencing of Discordant Monozygotic Twins Reveals Regulatory Variants Associated With Nonsyndromic Orofacial Clefts.Human mutation · 2026Article
- Clinical utility of DNA-methylation signatures in routine diagnostics for neurodevelopmental disorders.European journal of human genetics : EJHG · 2025Article
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- Role of artificial intelligence in advancing immunology.Immunologic research · 2025Review
- Detection of mRNA Transcript Variants.Genes · 2025Review
- Predicting the impact of rare variants on RNA splicing in CAGI6.Human genetics · 2025Article
- Toward a comprehensive profiling of alternative splicing proteoform structures, interactions and functions.Current opinion in structural biology · 2025Review
- Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects.Frontiers in pediatrics · 2025Review
- Germline Variants in Patients Affected by Both Uveal and Cutaneous Melanoma.Pigment cell & melanoma research · 2025Article
- Transformers significantly improve splice site prediction.Communications biology · 2024Article
- Homozygous variants in WDR83OS lead to a neurodevelopmental disorder with hypercholanemia.American journal of human genetics · 2024Article
- Functional characterization of 2,832 JAG1 variants supports reclassification for Alagille syndrome and improves guidance for clinical variant interpretation.American journal of human genetics · 2024Article
- An interpretable model of pre-mRNA splicing for animal and plant genes.Science advances · 2024Article
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4 authors.
Funding
Abstract
backgroundIt is estimated that up to 50% of all disease causing variants disrupt splicing. Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning missed diagnoses for patients. The emergence of machine learning for targeted medicine holds great potential to improve prediction of splice disrupting variants. The recently published SpliceAI algorithm utilises deep neural networks and has been reported to have a greater accuracy than other commonly used methods. METHODS AND
findingsThe original SpliceAI was trained on splice sites included in primary isoforms combined with novel junctions observed in GTEx data, which might introduce noise and de-correlate the machine learning input with its output. Limiting the data to only validated and manual annotated primary and alternatively spliced GENCODE sites in training may improve predictive abilities. All of these gene isoforms were collapsed (aggregated into one pseudo-isoform) and the SpliceAI architecture was retrained (CI-SpliceAI). Predictive performance on a newly curated dataset of 1,316 functionally validated variants from the literature was compared with the original SpliceAI, alongside MMSplice, MaxEntScan, and SQUIRLS. Both SpliceAI algorithms outperformed the other methods, with the original SpliceAI achieving an accuracy of ∼91%, and CI-SpliceAI showing an improvement at ∼92% overall. Predictive accuracy increased in the majority of curated variants.
conclusionsWe show that including only manually annotated alternatively spliced sites in training data improves prediction of clinically relevant variants, and highlight avenues for further performance improvements.
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