The paper introduces Flexynesis, a novel deep learning toolkit designed for the bulk integration of multi-omics data, primarily aimed at precision oncology applications. The authors developed Flexynesis to address the limitations of existing deep learning methods, which often lack transparency, modularity, and easy deployability for clinical and preclinical research. Flexynesis offers a comprehensive framework that streamlines data processing, allows users to choose from various deep learning or classical machine learning architectures, and supports diverse tasks like regression, classification, survival modeling, and biomarker discovery. The article showcases the tool's versatility through several use cases, including single-task and multi-task learning, and demonstrates its accessibility across platforms like PyPi, Bioconda, and the Galaxy Server. Ultimately, Flexynesis is presented as a flexible, reproducible, and accessible solution to enhance the utility of deep learning in multi-omics data analysis.

References:

• Uyar B, Savchyn T, Naghsh Nilchi A, et al. Flexynesis: A deep learning toolkit for bulk multi-omics data integration for precision oncology and beyond[J]. Nature Communications, 2025, 16(1): 8261.