Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico.
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico.
Bioinformatics. 2023 Sep 2;39(9). doi: 10.1093/bioinformatics/btad507.
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 Multi-Target Automated Tree Engine (MuTATE) 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.
综合多组学研究推动了疾病建模的进展，有助于实现有效的精准医学，但这对现有的机器学习方法提出了挑战，因为这些方法在多个临床终点方面的解释能力有限。自动化的综合疾病建模需要一种机器学习方法，该方法可以通过解释多个临床终点，同时识别疾病亚组及其定义的分子生物标志物。目前的工具仅限于单个终点或有限的变量类型，需要高级计算技能，并且需要资源密集型的手动专家解释。
我们开发了多目标自动化树引擎（Multi-Target Automated Tree Engine，MuTATE），用于自动化和综合分子建模，它支持用户友好的多目标决策树构建和可视化，以显示分子生物标志物与多个临床终点所描述的患者亚组之间的关系。MuTATE 在整个模型构建过程中纳入了多个目标，并允许对目标进行加权，从而构建可解释的决策树，深入了解疾病异质性和分子特征。MuTATE 消除了手动合成多个不可解释模型的需求，使其对生物信息学家和临床医生来说高效且易于使用。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，它可以通过为精准医学提供全面的分子见解，从而改善治疗决策。MuTATE 有可能改变生物标志物的发现和亚型的识别，从而在精准医学中实现更有效和个性化的治疗策略，并推进我们对疾病机制的分子水平的理解。
MuTATE 可在 GitHub（https://github.com/SarahAyton/MuTATE）上免费获得，许可证为 GPLv3。
