ReviewGenes2025
A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research.
Review in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders.Neuroscience and biobehavioral reviews · 2026Review
- Machine learning-based prediction of 3-month mortality in elderly patients with non-small cell lung cancer and bone metastases.Scientific reports · 2026Article
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- Toward trustworthy artificial intelligence in multi-omics: a review of reproducibility, stability, and interpretability.Briefings in bioinformatics · 2026Review
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Electrochemical Biosensors for Cancer Diagnosis and Prognosis Using Protein Biomarkers: Current Trends, Advances, and Clinical Translation Potential.Sensors (Basel, Switzerland) · 2026Review
- Organ-on-a-Chip Technology and Global Multi-Omics: Current Applications and Future Directions.MedComm · 2026Review
- Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.Frontiers in genetics · 2026Article
- Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights.Frontiers in microbiology · 2026Review
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Liquid Biopsy and Multi-Omic Biomarkers in Breast Cancer: Innovations in Early Detection, Therapy Guidance, and Disease Monitoring.Biomedicines · 2025Review
- Interpretable ensemble learning for tumor-type prediction with a SHAP-based evaluation of CatBoost and voting classifiers.Scientific reports · 2025Article
- Emerging hallmarks and the rise of complexities and heterogeneity of tumor.Biochemistry and biophysics reports · 2025Review
- Integrating New Approach Methodologies (NAMs) into Preclinical Regulatory Evaluation of Oncology Drugs.Biomimetics (Basel, Switzerland) · 2025Review
- AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.Clinical and experimental medicine · 2025Review
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Review
- Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of deep learning (DL) with multi-omics data has significantly advanced our understanding of biological systems, particularly in cancer research. DL enables the analysis of high-dimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and management. This review provides a detailed overview of recent developments in deep learning models applied to genomics data, with a focus on cancer type classification, driver gene identification, survival analysis, and drug response prediction. We introduce the foundational concepts of machine and deep learning and explain the characteristics of multi-omics data, addressing a broad and interdisciplinary audience. Methods published since 2020 are systematically reviewed, including their model architectures, datasets, and key innovations.
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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.