ArticleBMC bioinformatics2021
Spliceator: multi-species splice site prediction using convolutional neural networks.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 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.
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Who cites it
55 citing papers in PubMed.
- CRYSPER and CUREIPES: two new molecular tools enabling analysis of RNA splicing and C-to-U RNA editing-inducible protein expression at single cell resolution.Nucleic acids research · 2026Article
- An overview of self-supervised deep learning applications to molecular data.Briefings in bioinformatics · 2026Review
- Genetic Heterogeneity of Autism Spectrum Disorder: Identification of Five Novel Mutations (RIMS2, FOXG1, AUTS2, ZCCHC17, and SPTBN5) in Iranian Families via Whole-Exome and Whole-Genome Sequencing.Biochemical genetics · 2026Article
- Spliceread: improving canonical and non-canonical splice site prediction with residual blocks and synthetic data augmentation.BMC bioinformatics · 2026Article
- UniSplicer: A deep-learning framework for accurate splice-site prediction and splice-altering mutation detection across diverse taxa.Plant communications · 2026Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- Molecular characterization of Cdh12-SCON conditional knockout mice reveals unexpected splicing changes.Transgenic research · 2026Article
- Analysis of SalHV-1 Genes by Structure Prediction and Comparison Shows an Expanded Core Gene Set of the OrderViruses · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- Advancing knock-in approaches for robust genome editing in zebrafish.Biology open · 2026Article
- SpliceRead: Improving Canonical and Non-Canonical Splice Site Prediction with Residual Blocks and Synthetic Data Augmentation.bioRxiv : the preprint server for biology · 2026Article
- Decoding the interconnected splicing patterns of hepatitis B virus and host using large language and deep learning models.Microbial genomics · 2026Article
- BlendSplice: A Frequency-Blended Generative Framework forComputational and structural biotechnology journal · 2026Article
- ERNIE-RNA: an RNA language model with structure-enhanced representations.Nature communications · 2025Article
- HydraRNA: a hybrid architecture based full-length RNA language model.Genome biology · 2025Article
- Investigation of growth traits in Turkish Merino lambs using multi-locus GWAS approaches: Karacabey Merino.BMC veterinary research · 2025Article
- Synthesis of large single-transcript pathways from oligonucleotide pools: Design of STARBURST, an autobioluminescent reporter.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Genomic Characterization and Molecular Epidemiology of Tusaviruses and Related Novel Protoparvoviruses (FamilyViruses · 2025Article
- Predicting Protein Function in the AI and Big Data Era.Biochemistry · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundAb initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking.
resultsWe developed Spliceator to predict splice sites in a wide range of species, including model and non-model organisms. Spliceator uses a convolutional neural network and is trained on carefully validated data from over 100 organisms. We show that Spliceator achieves consistently high accuracy (89-92%) compared to existing methods on independent benchmarks from human, fish, fly, worm, plant and protist organisms.
conclusionsSpliceator is a new Deep Learning method trained on high-quality data, which can be used to predict splice sites in diverse organisms, ranging from human to protists, with consistently high accuracy.
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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 Socratic graph.