BreCol：基于微生物组的癌症检测的经典与深度学习方法基准测试
BreCol: Benchmarking Classical and Deep-Learning Methods for Microbiome-Based Cancer Detection
浏览论文内容
中文总结 AI 辅助
BreCol基准测试比较经典与深度学习方法在微生物组癌症检测中的性能，发现经典方法在保留数据上优于深度学习模型，并公开了2040次测序运行的数据与代码。
中文摘要 AI 辅助
对肠道微生物群落的DNA测序显示出癌症检测的前景，但跨研究结果的普遍性仍存疑问。我们提出了BreCol，一个包含26项研究、2040次16S rRNA基因测序运行的基准，涵盖乳腺癌、结直肠癌和健康队列。训练-测试划分在2023年前的研究内进行，而保留评估使用2023年及以后的研究，反映了与训练数据的时间分离。经典模型在癌症诊断上达到测试/保留AUC分别为0.77/0.60，在癌症类型预测上达到1.00/0.83。我们同时训练模型于两种癌症类型，发现结直肠癌通常比乳腺癌更容易检测。我们还评估了两种深度学习模型：HyenaDNA，一种长程序列模型，通过池化隐藏状态进行分类；以及SetBERT，一种在读取集合上生成上下文嵌入的Transformer。两种深度学习模型在保留数据上的表现均不如最佳经典方法，尽管调整训练集大小和分类头带来了适度的改进。我们的经典流程使用无监督聚类从四聚体频率中提取特征，保留了运行内的组成信号，并在不依赖分类学分配的情况下达到了接近最先进的性能。BreCol数据和相关代码公开可用。
英文摘要
DNA sequencing of the gut microbial community shows promise for cancer detection, but questions remain about the generalizability of results across studies. We propose BreCol, a benchmark of 2,040 16S rRNA gene sequencing runs across 26 studies spanning breast cancer, colorectal cancer, and healthy cohorts. Train-test splits are made within pre-2023 studies, while holdout evaluation uses studies from 2023 onward, reflecting temporal separation from training data. Classical models reach test/holdout AUCs of 0.77/0.60 for cancer diagnosis and 1.00/0.83 for cancer type prediction. We train the models on both cancer types simultaneously and find that colorectal cancer is often easier to detect than breast cancer. We also evaluate two deep learning models: HyenaDNA, a long-range sequence model that pools hidden states for classification, and SetBERT, a transformer that produces contextualized embeddings over sets of reads. Both deep learning models underperform the best classical methods on holdout data, though tuning training set size and the classification head yields modest gains. Our classical pipeline uses unsupervised clustering to derive features from tetramer frequencies, preserving within-run compositional signal and achieving near state-of-the-art performance without relying on taxonomic assignments. BreCol data and associated code are publicly available.
