ArticleNature communications2025
Flexynesis: A deep learning toolkit for bulk multi-omics data integration for precision oncology and beyond.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Review
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- EMMA-STRAT: a multi-omics based machine learning framework for stratification of endometrial carcinoma molecular subtypes and MSI status.BioData mining · 2026Article
- PIMO: pathway-based interpretable multiomics interactions for multiomics integration.Bioinformatics (Oxford, England) · 2026Article
- Deep Learning-Based Analysis of Gene Expression Data and Gene-Related Information in Pediatric Surgical Oncology: A Scoping Review.Cancer medicine · 2026Article
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- Digital immune twins and ai-integrated multi-omic biomarkers: Redefining personalized immunotherapy in non-small cell lung cancer.Iranian journal of basic medical sciences · 2026Review
- Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.American journal of clinical and experimental immunology · 2026Article
- Flexynesis: A deep learning toolkit for bulk multi-omics data integration for precision oncology and beyond.Nature communications · 2025Article
- Mechanisms and technologies in cancer epigenetics.Frontiers in oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate decision making in precision oncology depends on integration of multimodal molecular information, for which various deep learning methods have been developed. However, most deep learning-based bulk multi-omics integration methods lack transparency, modularity, deployability, and are limited to narrow tasks. To address these limitations, we introduce Flexynesis, which streamlines data processing, feature selection, hyperparameter tuning, and marker discovery. Users can choose from deep learning architectures or classical supervised machine learning methods with a standardized input interface for single/multi-task training and evaluation for regression, classification, and survival modeling. We showcase the tool's capability across diverse use-cases in precision oncology. To maximize accessibility, Flexynesis is available on PyPi, Guix, Bioconda, and the Galaxy Server ( https://usegalaxy.eu/ ). This toolset makes deep-learning based bulk multi-omics data integration in clinical/pre-clinical research more accessible to users with or without deep-learning experience. Flexynesis is available at https://github.com/BIMSBbioinfo/flexynesis .
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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.