ArticleBriefings in bioinformatics2020
A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping.
Article in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Transcriptomic and epigenomic insights into ovarian cancer: a bioinformatics perspective - a narrative review.Annals of medicine and surgery (2012) · 2026Article
- The human microbiome as a source of novel bioactive natural products: structures, bioactivities, and biosynthetic insights.Journal of natural medicines · 2026Review
- Multi-omics integration in allergy, viral infections and asthma: a comprehensive analysis of bioinformatics tools.Frontiers in bioinformatics · 2026Article
- The impact of epigenetics on tumor metabolism: Friend or foe in drug response?Frontiers in epigenetics and epigenomics · 2026Review
- Phenotypic stratification of Low-grade Glioma using multimodal MRI via outcome-weighted integrative clustering.BMC neurology · 2025Article
- Multi-omics approaches: transforming the landscape of natural product isolation.Functional & integrative genomics · 2025Review
- Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024Review
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- Genomic data integration tutorial, a plant case study.BMC genomics · 2024Article
- Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine.Frontiers in physiology · 2024Review
- Coordinated Regulation of Myonuclear DNA Methylation, mRNA, and miRNA Levels Associates With the Metabolic Response to Rapid Synergist Ablation-Induced Skeletal Muscle Hypertrophy in Female Mice.Function (Oxford, England) · 2024Article
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI.PLoS computational biology · 2023Article
- Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage.Briefings in bioinformatics · 2023Article
- Opportunities and challenges of 5G network technology toward precision medicine.Clinical and translational science · 2023Review
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- AKT and MAPK signaling pathways in hippocampus reveals the pathogenesis of depression in four stress-induced models.Translational psychiatry · 2023Article
- Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods.PloS one · 2023Article
- Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.
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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.