ArticleBriefings in bioinformatics2020
Consistency and overfitting of multi-omics methods on experimental data.
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 21 papers.
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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.
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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
21 citing papers in PubMed.
- Development and validation of a serum peptidomic signature for early detection of asymptomatic ovarian cancer: A multi-center prospective study.Cell reports. Medicine · 2026Article
- A Practical Roadmap for Clinical Translation of Metabolic Biomarkers: A Review.International journal of molecular sciences · 2026Review
- Multiomics Machine Learning to Predict Neoadjuvant Chemotherapy Outcome and Relapse of Breast Cancer.BME frontiers · 2026Article
- From empirical vaccinology to predictive systems-based vaccine design: multi-omics integration, artificial intelligence, and global equity challenges.Frontiers in systems biology · 2026Review
- Enhancement of the Antioxidant Activity ofMolecules (Basel, Switzerland) · 2025Article
- Navigating Cancer Complexity: Integrative Multi-Omics Methodologies for Clinical Insights.Clinical Medicine Insights. Oncology · 2025Review
- Limited generalizability of multivariate brain-based dimensions of child psychiatric symptoms.Communications psychology · 2024Article
- Integrative approaches based on genomic techniques in the functional studies on enhancers.Briefings in bioinformatics · 2023Review
- Transformer-Based Multi-Modal Data Fusion Method for COPD Classification and Physiological and Biochemical Indicators Identification.Biomolecules · 2023Article
- The Application of Spearman Partial Correlation for Screening Predictors of Weight Loss in a Multiomics Dataset.Omics : a journal of integrative biology · 2022Article
- Proarrhythmic risk assessment of drugs by dVCPT: pharmacometrics & systems pharmacology · 2022Article
- Interpretive JIVE: Connections with CCA and an application to brain connectivity.Frontiers in neuroscience · 2022Article
- Multi-Omics Approach to Elucidate Cerebrospinal Fluid Changes in Dogs with Intervertebral Disc Herniation.International journal of molecular sciences · 2021Article
- Community-wide hackathons to identify central themes in single-cell multi-omics.Genome biology · 2021Article
- Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer.BMC bioinformatics · 2021Article
- DNA methylation and gene expression integration in cardiovascular disease.Clinical epigenetics · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Integration strategies of multi-omics data for machine learning analysis.Computational and structural biotechnology journal · 2021Review
- EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models.Frontiers in genetics · 2021Article
- Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling.Frontiers in oncology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.
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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.