ReviewFrontiers in oncology2020
Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer.
Review in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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
75 citing papers in PubMed.
- Integrating ex vivo platforms with AI to guide glioblastoma treatment.Journal of neuro-oncology · 2026Review
- Molecular subtyping-guided precision therapy for ESCC: biomarker-driven strategies and clinical translation pathways.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial intelligence advances in cystoscopy and imaging for bladder cancer: a narrative review.The Canadian journal of urology · 2026Review
- MicroRNA Dysregulation in HPV-Driven Cervical Cancer: A Review of Oncoprotein-Targeted Signaling Pathways.Life (Basel, Switzerland) · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Nanomaterial-based total analysis systems for isolation and detection of exosomal biomarkers in cancer diagnosis.Materials today. Bio · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Transformer Models, Graph Networks, and Generative AI in Gut Microbiome Research: A Narrative Review.Bioengineering (Basel, Switzerland) · 2026Review
- Systems Biology and Multi-Omics in Asthma and COPD: A Systematic Review of Computational Approaches (2010-2024).Journal of asthma and allergy · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response.Bioinformatics advances · 2026Article
- Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.Frontiers in digital health · 2026Review
- Review
- Emerging technologies and clinical translation of urine-based liquid biopsy in urological cancers.Genes & genomics · 2025Review
- Research trends of neoadjuvant therapy for breast cancer: A bibliometric analysis.Human vaccines & immunotherapeutics · 2025Review
- Artificial Intelligence in Edible Mushroom Cultivation, Breeding, and Classification: A Comprehensive Review.Journal of fungi (Basel, Switzerland) · 2025Review
- Analysing the Structural Identifiability and Observability of Mechanistic Models of Tumour Growth.Bioengineering (Basel, Switzerland) · 2025Article
- Integrating Machine Learning and Multi-Omics to Explore Neutrophil Heterogeneity.Biomedicines · 2025Review
- Revolutionizing multi-omics analysis with artificial intelligence and data processing.Quantitative biology (Beijing, China) · 2025Review
- The dark matter in cancer immunology: beyond the visible- unveiling multiomics pathways to breakthrough therapies.Journal of translational medicine · 2025Review
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.
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.