In the field of sewage treatment, the identification of polyphosphate-accumulating organisms (PAOs) usually relies on biological experiments. However, biological experiments are not only complicated and time-consuming, but also costly. In recent years, machine learning has been widely used in many fields, but it is seldom used in the water treatment. The present work presented a high accuracy support vector machine (SVM) algorithm to realize the rapid identification and prediction of PAOs. We obtained 6,318 genome sequences of microorganisms from the publicly available microbial genome database for comparative analysis (MBGD). Minimap2 was used to compare the genomes of the obtained microorganisms in pairs, and read the overlap. The SVM model was established using the similarity of the genome sequences. In this SVM model, the average accuracy is 0.9628 ± 0.019 with 10-fold cross-validation. By predicting 2,652 microorganisms, 22 potential PAOs were obtained. Through the analysis of the predicted potential PAOs, most of them could be indirectly verified their phosphorus removal characteristics from previous reports. The SVM model we built shows high prediction accuracy and good stability.

在污水处理领域，多磷酸盐蓄积生物（PAOs）的鉴定通常依赖于生物学实验。然而，生物学实验不仅复杂且费时，而且成本高昂。近年来，机器学习已在许多领域中广泛使用，但很少在水处理中使用。目前的工作提出了一种高精度的支持向量机（SVM）算法，以实现对PAO的快速识别和预测。我们从公众可获得的微生物基因组数据库中获得了6,318个微生物的基因组序列，用于比较分析（MBGD）。Minimap2用于成对比较获得的微生物的基因组，并读取重叠。利用基因组序列的相似性建立SVM模型。在此SVM模型中，平均精度为0。9628±0.019，具有10倍交叉验证。通过预测2,652种微生物，获得了22种潜在的PAO。通过对预测的潜在PAO的分析，可以从以前的报告中间接验证它们中大多数的除磷特性。我们建立的SVM模型显示出较高的预测准确性和良好的稳定性。