Uyar Bora, Savchyn Taras, Naghsh Nilchi Amirhossein, Sarigun Ahmet, Wurmus Ricardo, Shaik Mohammed Maqsood, Grüning Björn, Franke Vedran, Akalin Altuna
Bioinformatics and Omics Data Science Platform, Max Delbruck Center for Molecular Medicine, The Berlin Institute for Molecular Systems Biology, Hannoversche Str. 28, 10115, Berlin, Germany.
Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, 79110, Freiburg, Germany.
Nat Commun. 2025 Sep 12;16(1):8261. doi: 10.1038/s41467-025-63688-5.
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 .
精准肿瘤学中的准确决策依赖于多模态分子信息的整合，针对此已开发了各种深度学习方法。然而，大多数基于深度学习的批量多组学整合方法缺乏透明度、模块化、可部署性，并且局限于狭窄的任务。为解决这些局限性，我们引入了Flexynesis，它简化了数据处理、特征选择、超参数调整和标志物发现。用户可以从深度学习架构或经典监督机器学习方法中进行选择，具有标准化输入接口，用于单任务/多任务训练以及回归、分类和生存建模的评估。我们展示了该工具在精准肿瘤学不同用例中的能力。为了最大限度地提高可及性，Flexynesis可在PyPi、Guix、Bioconda和Galaxy服务器（https://usegalaxy.eu/）上获取。此工具集使临床/临床前研究中基于深度学习的批量多组学数据整合对于有或没有深度学习经验的用户来说更容易获取。Flexynesis可在https://github.com/BIMSBbioinfo/flexynesis获取。
