ReviewPLoS computational biology2020
Multiview learning for understanding functional multiomics.
Review in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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
43 citing papers in PubMed.
- Latent variables for region of interest activation during the monetary incentive delay task.NeuroImage · 2021Trial
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- A unified analysis of atlas single-cell data.Genome research · 2025Article
- scDOT: optimal transport for mapping senescent cells in spatial transcriptomics.Genome biology · 2024Article
- Multi-class boosting for the analysis of multiple incomplete views on microbiome data.BMC bioinformatics · 2024Article
- Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer.Journal of personalized medicine · 2024Article
- BAG3 as a novel prognostic biomarker in kidney renal clear cell carcinoma correlating with immune infiltrates.European journal of medical research · 2024Article
- Article
- Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package.Scientific reports · 2023Article
- Applications for Deep Learning in Epilepsy Genetic Research.International journal of molecular sciences · 2023Review
- "iNETgrate": integrating DNA methylation and gene expression data in a single gene network.Research square · 2023Article
- Flow Cytometry: The Next Revolution.Cells · 2023Review
- Evidence for human milk as a biological system and recommendations for study design-a report from "Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)" Working Group 4.The American journal of clinical nutrition · 2023Article
- Molecular Landscape of Tourette's Disorder.International journal of molecular sciences · 2023Article
- Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics.Frontiers in immunology · 2023Article
- A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.Methods in molecular biology (Clifton, N.J.) · 2023Article
- How data science and AI-based technologies impact genomics.Singapore medical journal · 2023Review
- Review
- scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction.Bioinformatics (Oxford, England) · 2022Article
- Multi-omics disease module detection with an explainable Greedy Decision Forest.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.
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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.