ArticleiScience2022
DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- IRCAS: a novel end-to-end approach to identify, rectify, and classify comprehensive alternative splicing events in a transcriptome without genome reference.Briefings in bioinformatics · 2026Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- ASPECT: Alternative Splicing Event Classification with Transformers.bioRxiv : the preprint server for biology · 2026Article
- Detection of alternative splicing: deep sequencing or deep learning?Briefings in bioinformatics · 2026Article
- MkcDBGAS: a reference-free approach to identify comprehensive alternative splicing events in a transcriptome.Briefings in bioinformatics · 2023Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- Advances in alternative splicing identification: deep learning and pantranscriptome.Frontiers in plant science · 2023Article
- Methods for Analyzing Alternative Splicing and Its Regulation in Plants: From Gene-Specific Approaches to Transcriptome-Wide Studies.Physiologia plantarumReview
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Authors and funding
5 authors.
Funding
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Abstract
Alternative splicing is crucial for a wide range of biological processes. However, limited by the availability of reference genomes, genome-wide patterns of alternative splicing remain unknown in most nonmodel organisms. We present an attention-based convolutional neural network model, DeepASmRNA, for predicting alternative splicing events using only transcriptomic data. DeepASmRNA consists of two parts: identification of alternatively spliced transcripts and classification of alternative splicing events, which outperformed the state-of-the-art method, AStrap, and other deep learning models. Then, we utilize transfer learning to increase the performance in species with limited training data and use an interpretation method to decipher splicing codes. Finally, applying
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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 OpenQuestion graph.