ReviewCells2019
Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing.
Review in Cells, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 60 citations in OpenAlex.
- Contribution of Noncanonical Splice Variants toCirculation. Genomic and precision medicine · 2021Trial
- SpliceDetector: A Splice Sites Detection Method Based on Biology Attention and Ensemble Module.Interdisciplinary sciences, computational life sciences · 2026Article
- Rapid minigene workflow for functional reclassification of splicing variants in hereditary cancer diagnostics.Journal of medical genetics · 2026Article
- Genome-wide functional annotation and interpretation of splicing variants: toward RNA-targeted therapies.Journal of human genetics · 2025Review
- New genetic diagnoses for inherited retinal dystrophies by integrating splicing tools into NGS pipelines.NPJ genomic medicine · 2025Article
- Functional impact of splicing variants in the elaboration of complex traits in cattle.Nature communications · 2025Article
- Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms.Bioinformatics (Oxford, England) · 2025Article
- From computational models of the splicing code to regulatory mechanisms and therapeutic implications.Nature reviews. Genetics · 2025Review
- Homozygous variants in WDR83OS lead to a neurodevelopmental disorder with hypercholanemia.American journal of human genetics · 2024Article
- Pan-Cancer Profiling of Intron Retention and Its Clinical Significance in Diagnosis and Prognosis.Cancers · 2023Article
- Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis.Briefings in bioinformatics · 2023Review
- Identified eleven exon variants in PKD1 and PKD2 genes that altered RNA splicing by minigene assay.BMC genomics · 2023Article
- Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.European radiology experimental · 2023Article
- A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project.Genome medicine · 2022Article
- Recommendations for clinical interpretation of variants found in non-coding regions of the genome.Genome medicine · 2022Article
- MRSD: A quantitative approach for assessing suitability of RNA-seq in the investigation of mis-splicing in Mendelian disease.American journal of human genetics · 2022Article
- Identification of novel single-nucleotide variants altering RNA splicing of PKD1 and PKD2.Journal of human genetics · 2022Article
- Deep learning methods may not outperform other machine learning methods on analyzing genomic studies.Frontiers in genetics · 2022Article
- CI-SpliceAI-Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites.PloS one · 2022Article
- What's Wrong in a Jump? Prediction and Validation of Splice Site Variants.Methods and protocols · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient's variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care.
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What OpenQuestion holds
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.