Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models

Abstract

Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in this study, we predicted ARGs occurrence that are primarily found on the coast after rainfall using a conventional long short-term memory (LSTM), LSTMconvolutional neural network (CNN) hybrid model, and input attention (IA)-LSTM. To develop the models, 10 categories of environmental data collected at 30-min intervals and concentration data of 4 types of major ARGs (i.e., aac(6 -Ib-cr ), bla TEM , sul 1, and tet X) obtained at the Gwangalli Beach in South Korea, between 2018 and 2019 were used. When individually predicting ARGs occurrence, the conventional L STM and IA-L STM exhibited poor R 2 values during training and testing. In contrast, the LSTM-CNN exhibited a 2-6-times improvement in accuracy over those of the conventional L STM and IA-L STM. However, when predicting all ARGs occurrence simultaneously, the IA-LSTM model exhibited a superior performance overall compared to that of LSTM-CNN. Additionally, the influence of environmental variables on prediction was investigated using the IA-LSTM model, and the ranges of input variables that affect each ARG were identified. Consequently, this study demonstrated the possibility of predicting the occurrence and distribution of major ARGs at the beach based on various environmental variables, and the results are expected to contribute to management of ARG occurrence at a recreational beach.

AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods

“…2). The Experiment class can be sub-classed to We conducted an experiment to compare the performance of classic machine learning algorithms in predicting antibiotic-resistant genes (ARGs) at a recreational beach (Jang et al, 2021). The results of this experiment are shown in Fig.…”

Deep Learning and Antibiotic Resistance

“…All three models’ predictions were compared to the observed emergence of ARGs and thus “loss” values were calculated. One NN technology had an enhanced performance in detecting single ARGs, whereas another model showed superior performance in predicting multi-ARGs and allowed identification of the importance of the input variables [ 47 ].…”

Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning

“…During optimization, the learning rate was converged from the large value to the small value, implying that our model preferred the small step size when adjusting the weight and bias. Jang et al [89] and Yun et al [90] also recommended the smaller learning rate to simulate the water quality. In addition, the lookback also was the influential factor to the model result.…”

“…8 shows the attention score map to temporally interpret the attention LSTM model. The plots represent the weight of input data to affect the model output [89]. On the attention score map, the color bar indicates the importance of the dataset [64].…”