ArticleBMC bioinformatics2016
An integrative imputation method based on multi-omics datasets.
Article in BMC bioinformatics, 2016. 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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Who cites it
21 citing papers in PubMed.
- Exposome influences: a multi-omics perspective on the combined toxic effects of pharmaceuticals and personal care products in Alzheimer's disease.Frontiers in toxicology · 2026Review
- Surface Modification of Gold Nanoparticle Impacts Distinct Lipid Metabolism.Molecules (Basel, Switzerland) · 2025Article
- Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review.Frontiers in oral health · 2025Review
- Multi-omics Integrative Analysis for Incomplete Data Using WeightedJournal of agricultural, biological, and environmental statistics · 2025Article
- Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.Briefings in bioinformatics · 2023Article
- Metabolomics and modelling approaches for systems metabolic engineering.Metabolic engineering communications · 2022Review
- Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods.Environmental health perspectives · 2022Article
- TiMEG: an integrative statistical method for partially missing multi-omics data.Scientific reports · 2021Article
- An efficient ensemble method for missing value imputation in microarray gene expression data.BMC bioinformatics · 2021Article
- Identification of key candidate genes and pathways associated with colorectal aberrant crypt foci-to-adenoma-to-carcinoma progression.Gastroenterology and hepatology from bed to bench · 2021Article
- A novel computational strategy for DNA methylation imputation using mixture regression model (MRM).BMC bioinformatics · 2020Article
- A general framework for integrative analysis of incomplete multiomics data.Genetic epidemiology · 2020Article
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
- Multiview learning for understanding functional multiomics.PLoS computational biology · 2020Review
- The single-cell eQTLGen consortium.eLife · 2020Article
- A Review of Integrative Imputation for Multi-Omics Datasets.Frontiers in genetics · 2020Review
- DeepMF: deciphering the latent patterns in omics profiles with a deep learning method.BMC bioinformatics · 2019Article
- Making multi-omics data accessible to researchers.Scientific data · 2019Article
- Review
- Integrating omics datasets with the OmicsPLS package.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundIntegrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments. These missing values severely hinder integrative analysis of multi-omics data. Current imputation methods mainly focus on using single omics data while ignoring biological interconnections and information imbedded in multi-omics data sets.
resultsIn this study, a novel multi-omics imputation method was proposed to integrate multiple correlated omics datasets for improving the imputation accuracy. Our method was designed to: 1) combine the estimates of missing value from individual omics data itself as well as from other omics, and 2) simultaneously impute multiple missing omics datasets by an iterative algorithm. We compared our method with five imputation methods using single omics data at different noise levels, sample sizes and data missing rates. The results demonstrated the advantage and efficiency of our method, consistently in terms of the imputation error and the recovery of mRNA-miRNA network structure.
conclusionsWe concluded that our proposed imputation method can utilize more biological information to minimize the imputation error and thus can improve the performance of downstream analysis such as genetic regulatory network construction.
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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.