ArticleScientific reports2021
TiMEG: an integrative statistical method for partially missing multi-omics data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Integrated lipidome and miRNome analyses reveal sex-based differences in circulating extracellular vesicles of alcohol use disorder patients.Cell biology and toxicology · 2026Article
- Challenges and Opportunities in Multi-Omics Data Acquisition and Analysis: Toward Integrative Solutions.Biomolecules · 2026Review
- Latest Developments in Mass Spectrometry-Based Techniques for Metabolomics Analysis.Advances in experimental medicine and biology · 2026Review
- Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields.Genes · 2025Review
- Multi-omics analysis in inclusion body myositis identifies mir-16 responsible for HLA overexpression.Orphanet journal of rare diseases · 2025Article
- Multi-omics modality completion and knowledge distillation for drug response prediction in cervical cancer.Frontiers in oncology · 2025Article
- An updated overview of the search for biomarkers of osteoporosis based on human proteomics.Journal of orthopaedic translation · 2024Review
- SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data.ArXiv · 2024Article
- Multi Omics Applications in Biological Systems.Current issues in molecular biology · 2024Review
- CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data.Computers in biology and medicine · 2024Article
- How is Big Data reshaping preclinical aging research?Lab animal · 2023Review
- CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data.Research square · 2023Article
- Article
- Missing data in multi-omics integration: Recent advances through artificial intelligence.Frontiers in artificial intelligence · 2023Review
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Authors and funding
2 authors.
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Abstract
Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case-control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.
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Registered trials
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