Flexynesis: A deep learning toolkit for bulk multi-omics data integration for precision oncology and beyond

Bora Uyar (), Taras Savchyn, Amirhossein Naghsh Nilchi, Ahmet Sarigun, Ricardo Wurmus, Mohammed Maqsood Shaik, Björn Grüning, Vedran Franke and Altuna Akalin ()
Additional contact information
Bora Uyar: The Berlin Institute for Molecular Systems Biology
Taras Savchyn: The Berlin Institute for Molecular Systems Biology
Amirhossein Naghsh Nilchi: Albert-Ludwigs-University Freiburg
Ahmet Sarigun: The Berlin Institute for Molecular Systems Biology
Ricardo Wurmus: The Berlin Institute for Molecular Systems Biology
Mohammed Maqsood Shaik: The Berlin Institute for Molecular Systems Biology
Björn Grüning: Albert-Ludwigs-University Freiburg
Vedran Franke: The Berlin Institute for Molecular Systems Biology
Altuna Akalin: The Berlin Institute for Molecular Systems Biology

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: 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 .

Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-63688-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63688-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-63688-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().