ArticlePLoS computational biology2022
AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Joint clinical and molecular subtyping of COPD with variational autoencoders.Nature communications · 2026Article
- Multi-omics insights into immune tolerance at the maternal-fetal interface in recurrent pregnancy loss: mechanisms, integration challenges, and translational perspectives.Frontiers in immunology · 2026Review
- ZipAEr: A compressive convolutional autoencoder for high-dimensional spatial omics data at subcellular resolution.Research square · 2025Article
- Deep Phenotyping of Obesity: Electronic Health Record-Based Temporal Modeling Study.Journal of medical Internet research · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Optimizing Model Performance and Interpretability: Application to Biological Data Classification.Genes · 2025Article
- Deep phenotyping obesity using EHR data: Promise, Challenges, and Future Directions.medRxiv : the preprint server for health sciences · 2024Article
- Cancer molecular subtyping using limited multi-omics data with missingness.PLoS computational biology · 2024Article
- Supervised multiple kernel learning approaches for multi-omics data integration.BioData mining · 2024Article
- Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping.Briefings in bioinformatics · 2024Article
- Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.Annual review of biomedical data science · 2024Review
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Article
- Integrative approach of omics and imaging data to discover new insights for understanding brain diseases.Brain communications · 2024Review
- Article
- DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping.BioData mining · 2023Article
- The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises.Computational and structural biotechnology journal · 2023Review
- Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma.Biomedicines · 2022Article
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Abstract
In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation analysis (CCA) and partial least squares (PLS). However, information contained in one data type pertaining to the other data type may be complex and in nonlinear form. Deep learning provides a convenient alternative to extract low-dimensional nonlinear data embedding. In addition, the deep learning setup can naturally incorporate the effects of clinical confounding factors into the integrative analysis. Here we report a deep learning setup, named Autoencoder-based Integrative Multi-omics data Embedding (AIME), to extract data representation for omics data integrative analysis. The method can adjust for confounder variables, achieve informative data embedding, rank features in terms of their contributions, and find pairs of features from the two data types that are related to each other through the data embedding. In simulation studies, the method was highly effective in the extraction of major contributing features between data types. Using two real microRNA-gene expression datasets, one with confounder variables and one without, we show that AIME excluded the influence of confounders, and extracted biologically plausible novel information. The R package based on Keras and the TensorFlow backend is available at https://github.com/tianwei-yu/AIME.
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