目前基于液体活检数据的早期癌症检测研究通常依赖于现成的模型，并面临异构数据以及手动设计的具有不同参数设置的数据预处理流程的挑战。为了应对这些挑战，我们推出了 AutoCancer，这是一个自动化、多模式、可解释的基于 Transformer 的框架。该框架将特征选择、神经架构搜索和超参数优化集成到贝叶斯优化的统一优化问题中。综合实验表明，AutoCancer 在特定癌症类型和泛癌症分析中实现了准确的性能，在三个队列中优于现有方法。我们通过识别与非小细胞肺癌相关的关键基因突变来查明不同阶段和亚型的关键因素，进一步证明了 AutoCancer 的可解释性。 AutoCancer 的稳健性及其强大的可解释性，强调了其在早期癌症检测中的临床应用潜力。© 2024 作者。

Current studies in early cancer detection based on liquid biopsy data often rely on off-the-shelf models and face challenges with heterogeneous data, as well as manually designed data preprocessing pipelines with different parameter settings. To address those challenges, we present AutoCancer, an automated, multimodal, and interpretable transformer-based framework. This framework integrates feature selection, neural architecture search, and hyperparameter optimization into a unified optimization problem with Bayesian optimization. Comprehensive experiments demonstrate that AutoCancer achieves accurate performance in specific cancer types and pan-cancer analysis, outperforming existing methods across three cohorts. We further demonstrated the interpretability of AutoCancer by identifying key gene mutations associated with non-small cell lung cancer to pinpoint crucial factors at different stages and subtypes. The robustness of AutoCancer, coupled with its strong interpretability, underscores its potential for clinical applications in early cancer detection.© 2024 The Author(s).