Hernandez Jose Guadalupe, Saini Anil Kumar, Ghosh Attri, Moore Jason H
Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Patterns (N Y). 2025 Jul 11;6(7):101314. doi: 10.1016/j.patter.2025.101314.
The tree-based pipeline optimization tool (TPOT) is one of the earliest automated machine learning (ML) frameworks developed for optimizing ML pipelines, with an emphasis on addressing the complexities of biomedical research. TPOT uses genetic programming to explore a diverse space of pipeline structures and hyperparameter configurations in search of optimal pipelines. Here, we provide a comparative overview of the conceptual similarities and implementation differences between the previous and latest versions of TPOT, focusing on two key aspects: (1) the representation of ML pipelines and (2) the underlying algorithm driving pipeline optimization. We also highlight TPOT's application across various medical and healthcare domains, including disease diagnosis, adverse outcome forecasting, and genetic analysis. Additionally, we propose future directions for enhancing TPOT by integrating contemporary ML techniques and recent advancements in evolutionary computation.
基于树的管道优化工具（TPOT）是为优化机器学习（ML）管道而开发的最早的自动化机器学习框架之一，重点是解决生物医学研究的复杂性。TPOT使用遗传编程来探索管道结构和超参数配置的多样化空间，以寻找最优管道。在这里，我们对TPOT的先前版本和最新版本之间的概念相似性和实现差异进行了比较概述，重点关注两个关键方面：（1）ML管道的表示和（2）驱动管道优化的底层算法。我们还强调了TPOT在各种医学和医疗保健领域的应用，包括疾病诊断、不良结果预测和基因分析。此外，我们提出了通过整合当代ML技术和进化计算的最新进展来增强TPOT的未来方向。
