ArticleCancers2021
OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- Integrating Genomics, Radiomics, and Pathomics in Oncology: A Scoping Review and a Framework for AI-Enabled Surgomics.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Recent advances in omics-based research of mitral valve disease.Frontiers in cardiovascular medicine · 2026Review
- Multi-omics profiling and AI-driven clinically deployable risk models in MGUS and smoldering myeloma.Clinical and experimental medicine · 2025Review
- Review
- Molecular Network Analysis in Model and Non-Model Legumes: Challenges in Omics Data Interpretation Across Species, with a Focus onPlants (Basel, Switzerland) · 2025Review
- MOGEDN: small-sample cancer subtype classification with encoder-decoder networks for missing-omics recovery and biomarker discovery.Briefings in bioinformatics · 2025Article
- Transcriptome Transformer: improving patient survival prediction via multitask learning of transcriptomic and clinical features.Briefings in bioinformatics · 2025Article
- Joint similarity nonnegative matrix factorization model for identification of recurrence-related association patterns in tumor.Briefings in bioinformatics · 2025Article
- Multi-task machine learning for transfusion decision support in acute upper gastrointestinal bleeding: a novel ensemble approach with clinical validation.Journal of translational medicine · 2025Article
- Can classical statistics and deep learning converge on explainable, causally driven target discovery?DNA research : an international journal for rapid publication of reports on genes and genomes · 2025Review
- MODA: a graph convolutional network-based multi-omics integration framework for unraveling hub molecules and disease mechanisms.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called "the curse of dimensionality" in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.
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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.