ReviewAdvances in cancer research2024
Multi-omics based artificial intelligence for cancer research.
Review in Advances in cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- 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
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Circulating Tumor DNA in Ovarian Cancer: Emerging Roles in Early Detection, Risk Stratification, and Disease Monitoring.Cancers · 2026Review
- Bioinformatics analysis of TRPV1 expression and its role in hepatocellular carcinoma prognosis.Discover oncology · 2026Article
- Pediatric Hepatoblastoma: From Developmental Molecular Mechanisms to Innovative Therapeutic Strategies.Cancers · 2026Review
- Immunological Analysis of Oral Cytobrush Specimens for Early Detection of Oral Cancer Biomarkers: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Multi-omics fusion network for prediction of early recurrence in colorectal liver metastases.NPJ precision oncology · 2026Article
- Editorial: Next-generation technologies and multidisciplinary integration for oral cancer diagnosis and treatment.Frontiers in oral health · 2026Article
- From Variant to Biomarker in NSCLC Immunotherapy Resistance: Multiomics Evidence Chains and Accountable AI Integration.Human mutation · 2026Review
- Exosomal microRNAs in colorectal cancer communication networks: implications for metastasis, therapy resistance, and precision medicine.Frontiers in immunology · 2026Review
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- Transforming multi-omics data into images for disease classification: A review of techniques and tools.Journal of pathology informatics · 2026Review
- Gradient Descent to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Multimodal data integration in orthopedic regenerative medicine: bridging imaging, omics, and clinical data.Frontiers in cell and developmental biology · 2026Review
- MOTLAB: A Weighted Multi-Omics Transfer Learning Approach to Mitigate Breast Cancer Racial Disparities.bioRxiv : the preprint server for biology · 2025Article
- A Multi-Modal Transfer Learning Framework to Reduce Health Disparities in Prostate Adenocarcinoma.bioRxiv : the preprint server for biology · 2025Article
- Molecular life sciences in the era of the Fourth Industrial Revolution: sequencing, multi-omics and artificial intelligence.Emerging topics in life sciences · 2025Review
- Minimal Residual Disease Detection: Bridging Molecular and Clinical Strategies for Recurrence Prevention in Gynecologic Cancers.International journal of molecular sciences · 2025Review
- AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.Clinical and experimental medicine · 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
4 authors.
Funding
Abstract
With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.
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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.