综合多组学研究推动了疾病建模在有效精准医学领域的进展，但对现有的机器学习方法提出了挑战，因为在临床终点上它们具有有限的可解释性。自动化、综合的疾病建模需要一种机器学习方法，能够同时通过解释多个临床终点来识别疾病亚群和其定义的分子生物标志物。目前的工具仅限于个别终点或有限的变量类型，需要高级计算技能，并要求资源密集的手动专家解释。我们开发了MuTATE [多目标自动化树引擎]，用于自动化和综合的分子建模，使用户友好地进行多目标决策树构建，并可视化分子生物标志物和由多个临床终点表征的患者亚群之间的关系。MuTATE在模型构建过程中融入了多个目标，并允许目标权重，从而构建了可解释的决策树，为疾病异质性和分子特征提供了深入的见解。MuTATE消除了手动综合多个无法解释的模型的需要，使其对生物信息学家和临床医生非常高效和易用。MuTATE的灵活性和多功能性使其适用于广泛的复杂疾病，包括癌症，在这些疾病中，它可以通过为精准医学提供全面的分子洞察，改善治疗决策。MuTATE有潜力改变生物标志物发现和亚型鉴定，为精准医学中更有效和个性化的治疗策略提供支持，并推动了对疾病机制在分子水平上的理解。MuTATE可以在GitHub（https://github.com/SarahAyton/MuTATE）上免费获取，使用GPLv3许可证。补充数据可在Bioinformatics在线上获取。© The Author(s) 2023. Published by Oxford University Press.

Comprehensive multi-omics studies have driven advances in disease modeling for effective precision medicine but pose a challenge for existing machine learning approaches, which have limited interpretability across clinical endpoints. Automated, comprehensive disease modeling requires a machine learning approach that can simultaneously identify disease subgroups and their defining molecular biomarkers by explaining multiple clinical endpoints. Current tools are restricted to individual endpoints or limited variable types, necessitate advanced computation skills, and require resource-intensive manual expert interpretation.We developed MuTATE [Multi-Target Automated Tree Engine] for automated and comprehensive molecular modeling which enables user-friendly multi-objective decision tree construction and visualization of relationships between molecular biomarkers and patient subgroups characterized by multiple clinical endpoints. MuTATE incorporates multiple targets throughout model construction and allows for target weights, enabling construction of interpretable decision trees that provide insights into disease heterogeneity and molecular signatures. MuTATE eliminates the need for manual synthesis of multiple non-explainable models, making it highly efficient and accessible for bioinformaticians and clinicians. The flexibility and versatility of MuTATE make it applicable to a wide range of complex diseases, including cancer, where it can improve therapeutic decisions by providing comprehensive molecular insights for precision medicine. MuTATE has the potential to transform biomarker discovery and subtype identification, leading to more effective and personalized treatment strategies in precision medicine, and advancing our understanding of disease mechanisms at the molecular level.MuTATE is freely available at GitHub (https://github.com/SarahAyton/MuTATE) under the GPLv3 license.Supplementary data are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.