Lassen Johan K, Villesen Palle
Bioinformatics Research Center, Aarhus University Universitetsbyen 81, 3. Building 1872, 8000 Aarhus C, Denmark.
Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus N, Denmark.
Anal Chem. 2025 Feb 11;97(5):2795-2800. doi: 10.1021/acs.analchem.4c05113. Epub 2025 Feb 2.
Mass spectrometry is used to determine infectious microbial species in thousands of clinical laboratories across the world. The vast amount of data allows modern data analysis methods that harvest more information and potentially answer new questions. Here, we present an end-to-end deep learning model for predicting antibiotic resistance using raw matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) data. We used a 1-dimensional convolutional neural network to model (almost) raw data, skipping conventional peak-picking and directly predict resistance. The model's performance is state-of-the-art, having AUCs between 0.93 and 0.99 in all antimicrobial resistance phenotypes and validates across time and location. Feature attribution values highlight important insights into the model and how the end-to-end workflow can be improved further. This study showcases that reliable resistance phenotyping using MALDI-MS data is attainable and highlights the gains of using end-to-end deep learning for spectrometry data.
质谱分析法被全球数千家临床实验室用于确定感染性微生物种类。海量数据使得现代数据分析方法能够获取更多信息，并有可能回答新问题。在此，我们提出了一种端到端深度学习模型，用于使用原始基质辅助激光解吸电离质谱（MALDI-MS）数据预测抗生素耐药性。我们使用一维卷积神经网络对（几乎）原始数据进行建模，跳过传统的峰提取步骤，直接预测耐药性。该模型的性能处于当前先进水平，在所有抗菌药物耐药表型中的曲线下面积（AUC）在0.93至0.99之间，并且在不同时间和地点均得到验证。特征归因值突出了对该模型的重要见解以及端到端工作流程如何进一步改进。这项研究表明，使用MALDI-MS数据进行可靠的耐药表型分析是可行的，并突出了将端到端深度学习用于光谱数据的优势。
