摘要
      抗生素耐药性的日益流行已成为全球健康危机。为了安全监管的目的，鉴定细菌中的抗生素抗性基因（ARGs）具有非常重要的意义。尽管基于文化的方法可以相对更准确地识别ARG，但识别过程耗时且需要专业知识。随着全基因组测序技术的快速发展，研究人员试图通过计算公共数据库中的序列相似性来识别ARGs。然而，由于与已知ARG的序列同一性较低，这些计算方法可能无法检测到ARG。此外，现有的方法不能有效地解决ARGs的多药耐药性预测问题，这对临床治疗是一个巨大的挑战。为了应对这些挑战，我们提出了一个用于预测ARGs的端到端多标签学习框架。更具体地说，将ARGs预测任务建模为多标签学习问题，并提出了一种基于深度神经网络的端到端框架，其中引入了特定的损失函数，以利用多标签学习的优势进行ARGs预测。此外，还采用了双视图建模机制，充分利用了ARG的两个视图之间的语义关联，即基于序列的信息和基于结构的信息。在公开的数据上进行了广泛的实验，实验结果证明了所提出的框架在ARGs预测任务上的有效性。
Abstract
The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.

https://academic.oup.com/bib/article-abstract/23/3/bbac052/6546259?login=false