ArticleiScience2021
FusionAI: Predicting fusion breakpoint from DNA sequence with deep learning.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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
11 citing papers in PubMed.
- Cancer-associated fusion transcripts: mechanisms, functional roles, and clinical implications.Clinical and experimental medicine · 2026Review
- PFGPred: a stack ensemble classifier for the identification of fusion genes in plants.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026Article
- DeepECC: a deep learning framework for genome-wide identification and analysis of human cancer eccDNAs.Nucleic acids research · 2026Article
- Case Report: Novel ASAP1::BRAF fusion in a young adult with low-grade temporal lobe glioma.Frontiers in oncology · 2026Article
- Artificial intelligence in genomics: improving detection of fusion genes in soft tissue sarcomas.Annals of medicine and surgery (2012) · 2026Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- PeculiarGenes · 2024Article
- Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer - bioinformatics study for a potential link to MMEJ.Briefings in bioinformatics · 2023Article
- Article
- FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction.STAR protocols · 2022Article
- FusionGDB 2.0: fusion gene annotation updates aided by deep learning.Nucleic acids research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Identifying the molecular mechanisms related to genomic breakage is an important goal of cancer mechanism studies. Among diverse locations of structural variants, fusion genes, which have the breakpoints in the gene bodies and are typically identified from the split reads of RNA-seq data, can provide a highlighted structural variant resource for studying the genomic breakages with expression and potential pathogenic impacts. In this study, we developed FusionAI, which utilizes deep learning to predict gene fusion breakpoints based on DNA sequence and let us identify fusion breakage code and genomic context. FusionAI leverages the known fusion breakpoints to provide a prediction model of the fusion genes from the primary genomic sequences via deep learning, thereby helping researchers a more accurate selection of fusion genes and better understand genomic breakage.
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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.