ReviewComputational and structural biotechnology journal2024
Deep Learning of radiology-genomics integration for computational oncology: A mini review.
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Applications of machine learning and natural language processing to neurocognitive outcomes in posttreatment cancer survivors: a scoping review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- PRDM1 Is Associated with Chemoradiotherapy-Associated Enrichment of Adaptive NK Cells in Cervical Cancer.Computational and structural biotechnology journal · 2026Article
- An Interpretable Omics-to-Image Transformer Framework for Cancer Prognosis Prediction.Computational and structural biotechnology journal · 2026Article
- Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder.Scientific reports · 2025Article
- Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary.Sleep · 2025Article
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.Journal of cellular and molecular medicine · 2024Article
- Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.
Indexed as
Identifiers
What Socratic 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 Socratic graph.