SynthesisBriefings in bioinformatics2025
Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.
Synthesis in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- Toward trustworthy artificial intelligence in multi-omics: a review of reproducibility, stability, and interpretability.Briefings in bioinformatics · 2026Review
- Biomarkers of Treatment Response in Paediatric Medulloblastoma.Diagnostics (Basel, Switzerland) · 2026Review
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification.Frontiers in artificial intelligence · 2026Article
- Genomic innovations in cancer prevention, diagnosis, prognosis and precision therapeutics.Frontiers in genetics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discrepancies in cancer sequencing data continue to pose significant challenges for accurate mutation detection, potentially resulting in misdiagnoses and suboptimal treatment strategies. Although deep learning (DL) has emerged as a transformative approach for identifying and rectifying these errors, there remains a lack of comprehensive evaluation of DL architectures, performance benchmarks, and clinical translation. In this systematic review of 78 studies (2015-2024), We synthesize recent advancements in DL methodologies for identifying genomic discrepancies, demonstrating that convolutional and graph-based architectures currently achieve state-of-the-art performance in variant calling and tumor stratification. DL models reduce false-negative rates by 30%-40% compared to traditional pipelines, with methods such as MAGPIE prioritizing pathogenic variants with 92% accuracy. However, challenges such as data scarcity, batch effects, and the interpretability of "black-box" models persist. We propose a future research roadmap advocating federated learning to enhance data privacy and attention mechanisms to improve model transparency. By bridging bioinformatics and oncology, this review offers actionable insights to expedite the deployment of DL in precision cancer therapy.
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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.