ArticleBioinformatics (Oxford, England)2020
DEEPrior: a deep learning tool for the prioritization of gene fusions.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed, 22 citations in OpenAlex.
- 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
- Oncogenic gene fusions in cancer: from biology to therapy.Signal transduction and targeted therapy · 2025Review
- Seven quick tips for gene-focused computational pangenomic analysis.BioData mining · 2024Article
- Detection of novel PPP1R1B::STARD3 fusion transcript in acute myeloid leukemia: a case report.Journal of medical case reports · 2024Article
- Article
- Review
- Improving CNNs classification with pathologist-based expertise: the renal cell carcinoma case study.Scientific reports · 2023Article
- In silico validation of RNA-Seq results can identify gene fusions with oncogenic potential in glioblastoma.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
summaryIn the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user. AVAILABILITY AND IMPLEMENTATION: Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.