该文章介绍了 Flexynesis，这是一个用于批量多组学数据整合的深度学习工具包，旨在解决现有方法在透明度、模块化和部署方面的不足。该工具简化了数据处理、特征选择和模型训练，允许用户选择深度学习或传统机器学习方法来执行回归、分类和生存建模等任务。文章通过各种肿瘤学应用案例展示了 Flexynesis 在 单任务和多任务学习、无监督聚类以及 跨模态预测 等方面的能力。研究人员还提供了一个基准测试流程，强调了模型灵活性和任务特异性的重要性，以帮助用户根据特定数据集选择最佳方法。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.
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