高通量测序技术的广泛使用彻底改变了对生物学和癌症异质性的理解。最近，开发了几种基于转录数据的机器学习模型来准确预测患者的结果和临床反应。然而，涵盖最先进的机器学习算法以方便用户访问的开源 R 包尚未开发出来。因此，我们提出了一种灵活的计算框架来构建具有优雅性能的基于机器学习的集成模型（Mime）。 Mime 简化了开发高精度预测模型的过程，利用复杂的数据集来识别与预后相关的关键基因。 Mime 构建的基于 de novo PIEZO1 相关特征的计算机组合模型与其他已发表的模型相比，在预测患者结果方面表现出较高的准确性。此外，PIEZO1 相关签名还可以通过在 Mime 中应用不同的算法来精确推断免疫治疗反应。最后，从 PIEZO1 相关特征中选择的 SDC1 表现出作为神经胶质瘤靶标的巨大潜力。总而言之，我们的软件包提供了一个用户友好的解决方案，用于构建基于机器学习的集成模型，并将大大扩展以提供对当前领域的宝贵见解。 Mime 包可在 GitHub (https://github.com/l-magnificence/Mime) 上获取。© 2024 作者。

The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime).© 2024 The Authors.