ArticleSTAR protocols2022
FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction.
Article in STAR protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed.
- Characteristics of fusion genes in breast cancer.Cancer cell international · 2026Review
- Benchmarking genomic foundation models for binary classification of gene fusion breakpoints from DNA sequences.BioData mining · 2026Article
- Case Report: Novel ASAP1::BRAF fusion in a young adult with low-grade temporal lobe glioma.Frontiers in oncology · 2026Article
- Oncogenic gene fusions in cancer: from biology to therapy.Signal transduction and targeted therapy · 2025Review
- Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective.Clinical and translational medicine · 2024Review
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
Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).
Indexed as
Identifiers
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.