在Poore等人发表于《自然》（Nature）杂志上的一篇文章中，通过机器学习模型，研究结果表明，基于微生物组成，肿瘤类型之间几乎可以完美地进行区分。尽管我们认为微生物组成有潜力以这种方式被应用，但我们对该论文存在一些问题，使我们对所得结论的确定性产生质疑。我们认为有关污染贡献、处理批次效应、错误的阳性分类以及机器学习方法的局限性等方面存在问题。这使得很难确定作者是否已经发现了真正的生物信号，以及这些模型在临床生物标志物应用中的稳健性。我们赞扬Poore等人对开放数据和可重复性的处理方式，这为本次分析提供了基础。我们希望这种讨论能有助于未来机器学习模型和微生物组研究的假设生成的发展。

Results published in an article by Poore et al. (Nature. 2020;579:567-574) suggested that machine learning models can almost perfectly distinguish between tumour types based on their microbial composition using machine learning models. Whilst we believe that there is the potential for microbial composition to be used in this manner, we have concerns with the paper that make us question the certainty of the conclusions drawn. We believe there are issues in the areas of the contribution of contamination, handling of batch effects, false positive classifications and limitations in the machine learning approaches used. This makes it difficult to identify whether the authors have identified true biological signal and how robust these models would be in use as clinical biomarkers. We commend Poore et al. on their approach to open data and reproducibility that has enabled this analysis. We hope that this discourse assists the future development of machine learning models and hypothesis generation in microbiome research.