ReviewInternational journal of molecular sciences2021
A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.
Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 28 citations in OpenAlex.
- Deep Generative and Graph-Based Representation Learning for Multiomics Survival Stratification in Ovarian Cancer: Secondary Analysis.JMIR bioinformatics and biotechnology · 2026Article
- Radon-Induced Radiation Biomarkers: A Scoping Review from Exposure Dosimetry to Early Biological Effects on the Lung.International journal of molecular sciences · 2026Article
- Long noncoding RNAs in acute myeloid leukemia: biomarkers, prognostic indicators, and treatment potential.Cancer cell international · 2025Review
- mastR: an R package for automated identification of tissue-specific gene signatures in multi-group differential expression analysis.Bioinformatics (Oxford, England) · 2025Article
- PLASMA: Partial LeAst Squares for Multiomics Analysis.Cancers · 2025Article
- Deciphering molecular pathways driving cancer invasion and metastasis: advances and therapeutic prospects.Frontiers in oncology · 2025Review
- Regulatory Effects of SLC7A2-CPB2 on Lymphangiogenesis: A New Approach to Suppress Lymphatic Metastasis in HNSCC.Cancer medicine · 2024Article
- Article
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- Mechanisms and technologies in cancer epigenetics.Frontiers in oncology · 2024Review
- CRISPR-Based Therapies: Revolutionizing Drug Development and Precision Medicine.Current gene therapy · 2024Review
- Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer.International journal of molecular sciences · 2023Review
- A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis.Journal of translational medicine · 2023Article
- RAF1 promotes lymphatic metastasis of hypopharyngeal carcinoma via regulating LAGE1: an experimental research.Journal of translational medicine · 2022Article
- From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer.International journal of molecular sciences · 2022Review
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.
Indexed as
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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.