ReviewFrontiers in genetics2024
Methods for multi-omic data integration in cancer research.
Review in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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.
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Who cites it
30 citing papers in PubMed.
- Transcriptomic and epigenomic insights into ovarian cancer: a bioinformatics perspective - a narrative review.Annals of medicine and surgery (2012) · 2026Article
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- From Genes to Proteins: The Indispensable Role of Proteogenomics in Advancing Clear Cell Renal Cell Carcinoma Research.International journal of molecular sciences · 2026Review
- A DNA Methylation Signature Predicts Survival and Platinum Response in HNSCC.Laryngoscope investigative otolaryngology · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Radon-Induced Radiation Biomarkers: A Scoping Review from Exposure Dosimetry to Early Biological Effects on the Lung.International journal of molecular sciences · 2026Article
- Multi-omics approaches reveal erythroid progenitor cell in cancer: from passive bystander to active player.Oncogene · 2026Review
- The crosstalk between epigenetics and metabolism in the malignant cell.Discover oncology · 2026Review
- The evolving role of OMICS in gastrointestinal tumor biology and clinical practice.Molecular cancer · 2026Review
- Epigenetic Biomarkers for Predicting Nucleoside Analog Drug Response and Resistance in Cancer.Biomolecules · 2026Review
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Review
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- Unlocking the power of extracellular vesicles: multi-omics integration for cancer biomarker discovery.Biomarker research · 2026Review
- Integration of high-throughput proteomic data and complementary omics layers with PriOmics.Genome research · 2026Article
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Precision nutrition in gastric cancer: current advances and future directions.Frontiers in nutrition · 2026Review
- Omics and Multiomics-Based Diagnostics for Invasive Candidiasis: Toward Precision Medicine.Molecular & cellular proteomics : MCP · 2025Review
- Multi-omic data integration and exploiting metabolic models using systems biology approach increase precision in subtyping and early diagnosis of cancer.Quantitative biology (Beijing, China) · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-omics data integration is a term that refers to the process of combining and analyzing data from different omic experimental sources, such as genomics, transcriptomics, methylation assays, and microRNA sequencing, among others. Such data integration approaches have the potential to provide a more comprehensive functional understanding of biological systems and has numerous applications in areas such as disease diagnosis, prognosis and therapy. However, quantitative integration of multi-omic data is a complex task that requires the use of highly specialized methods and approaches. Here, we discuss a number of data integration methods that have been developed with multi-omics data in view, including statistical methods, machine learning approaches, and network-based approaches. We also discuss the challenges and limitations of such methods and provide examples of their applications in the literature. Overall, this review aims to provide an overview of the current state of the field and highlight potential directions for future research.
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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.