ArticlePLoS computational biology2021
Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
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Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.Journal of translational medicine · 2025Pooled it
- Biomarker discovery and patient stratification in pancreatic cancer using incomplete multi-omics data.PLoS computational biology · 2026Article
- Visual prompt engineering for multimodal and irregularly sampled medical data.Communications medicine · 2026Article
- A Bioinformatics Analysis Based on Omics and Clinical Data for Graph-Based Patient Stratification in Hepatocellular Carcinoma.Biology · 2026Article
- CAECC-Subtyper: A Novel Convolutional Autoencoder Framework for Integrating Multi-omics Data in Cancer Subtyping.Biochemical genetics · 2026Article
- Decoupled contrastive multi-view clustering with adaptive false negative elimination for cancer subtyping.PLoS computational biology · 2025Article
- Integrating multi-omics data: Methods and applications in human complex diseases.Biotechnology reports (Amsterdam, Netherlands) · 2025Review
- Refining treatment strategies for non-small cell lung cancer lacking actionable mutations: insights from multi-omics studies.British journal of cancer · 2025Review
- Multi-omics data integration for enhanced cancer subtyping via interactive multi-kernel learning.Briefings in bioinformatics · 2025Article
- MPAC: a computational framework for inferring pathway activities from multi-omic data.Bioinformatics (Oxford, England) · 2025Article
- A multi-omics integration framework using multi-label guided learning and multi-scale fusion.Briefings in bioinformatics · 2025Article
- Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset.Briefings in bioinformatics · 2025Article
- A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets.BMC genomics · 2025Review
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- EMitool: Explainable Multi-Omics Integration for Disease Subtyping.International journal of molecular sciences · 2025Article
- A generalized higher-order correlation analysis framework for multi-omics network inference.PLoS computational biology · 2025Article
- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
- miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources.Bioinformatics (Oxford, England) · 2025Article
- Clustering Analysis of Multiple Omics Data Types Identifies Cancer Patients With Consistent Survival Outcomes.Cancer informatics · 2025Article
Corrections and comments
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.
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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.