ArticleProceedings of the National Academy of Sciences of the United States of America2023
Deciphering RNA splicing logic with interpretable machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.Genome biology · 2026Article
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- Predicting human mRNA isoform levels from site-specific splicing kineticsbioRxiv : the preprint server for biology · 2026Article
- An in vivo parallelized reporter assay to uncover tissue-specific splicing regulatory sequences in a multicellular animal.Nucleic acids research · 2026Article
- RNA localization to nuclear speckles follows splicing logic.Nucleic acids research · 2026Article
- Massively parallel assay of human splice variants reveals cis-regulatory drivers of disease-associated and cell type-specific splicing regulation.bioRxiv : the preprint server for biology · 2025Article
- A basic framework to explain splice-site choice in eukaryotes.Nature communications · 2025Article
- Article
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis.Developmental cell · 2025Article
- Massive experimental quantification allows interpretable deep learning of protein aggregation.Science advances · 2025Article
- From computational models of the splicing code to regulatory mechanisms and therapeutic implications.Nature reviews. Genetics · 2025Review
- Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks.IBRO neuroscience reports · 2024Article
- Dynamics of RNA localization to nuclear speckles are connected to splicing efficiency.Science advances · 2024Article
- Decoding biology with massively parallel reporter assays and machine learning.Genes & development · 2024Review
- Massive experimental quantification of amyloid nucleation allows interpretable deep learning of protein aggregation.bioRxiv : the preprint server for biology · 2024Article
- Embracing exascale computing in nucleic acid simulations.Current opinion in structural biology · 2024Review
- Article
- Comparison of the Capacity of Several Machine Learning Tools to Assist Immunofluorescence-Based Detection of Anti-Neutrophil Cytoplasmic Antibodies.International journal of molecular sciences · 2024Article
- Deciphering RNA splicing logic with interpretable machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: Despite their excellent accuracy, they cannot describe how they arrived at their predictions. Here, using an "interpretable-by-design" approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed uncharacterized components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.
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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.