Department of Biological Sciences, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
Sci Rep. 2024 Oct 29;14(1):25951. doi: 10.1038/s41598-024-75973-2.
Minimum inhibitory concentration (MIC) denotes the in vitro benchmark indicating the quantity of antibiotic required to inhibit proliferation of specific bacterial strains. Determining MIC values corresponding to the infecting bacterial strain is paramount for tailoring appropriate antibiotic therapy. In the interim between specimen collection and laboratory-derived MIC outcomes, clinicians frequently resort to empirical therapy informed by retrospective analyses. Here introduces two deep learning approaches, a Convolutional Neural Network (CNN)-based model and an Enformer-based model, integrating genomic data of Klebsiella Pneumoniae and molecular structural data of 20 antibiotics to anticipate the MIC value of the bacterium for each antibiotic under consideration. These models demonstrate enhanced raw accuracy over the existing state-of-the-art model, which rely exclusively on genomic data. The CNN-based model achieves a notable 20% increase in raw accuracy while further mirroring the 1-tier accuracy of the state-of-the-art model. Although the Enformer-based model does not quite reach the performance levels of the CNN-based model, it offers an advantage by eliminating the need for arbitrary data processing steps. This streamlining of the data processing pipeline facilitates fast updates and improves the model interpretability. It is expected that these deep learning paradigms can significantly inform and bolster clinician decision-making during the empirical treatment phase.
最小抑菌浓度 (MIC) 是体外指标，用于表示抑制特定细菌菌株增殖所需的抗生素量。确定与感染细菌株相对应的 MIC 值对于定制适当的抗生素治疗至关重要。在标本采集和实验室得出 MIC 结果之间的这段时间里，临床医生经常根据回顾性分析来进行经验性治疗。本文提出了两种深度学习方法，一种是基于卷积神经网络 (CNN) 的模型，另一种是基于 Enformer 的模型，它们整合了肺炎克雷伯菌的基因组数据和 20 种抗生素的分子结构数据，以预测每种考虑中的抗生素对细菌的 MIC 值。这些模型在仅依赖基因组数据的现有最先进模型的基础上，提高了原始准确性。基于 CNN 的模型的原始准确性显著提高了 20%，并且进一步反映了最先进模型的 1 级准确性。尽管基于 Enformer 的模型没有达到基于 CNN 的模型的性能水平，但它通过消除对任意数据处理步骤的需求提供了优势。这种简化的数据处理管道使模型能够快速更新，并提高了模型的可解释性。预计这些深度学习范例可以在经验性治疗阶段为临床医生的决策提供重要信息和支持。
