Classification of bacterial plasmid and chromosome derived sequences using machine learning

Accurate Diagnosis of Lower Respiratory Infections Using Host Response and Respiratory Microbiome from a Single Metatranscriptome Test of Bronchoalveolar Lavage Fluid

Lower respiratory tract infections (LRTIs) diagnosis is challenging because noninfectious diseases mimic its clinical features. The altered host response and respiratory microbiome following LRTIs have the potential to differentiate LRTIs from noninfectious respiratory diseases (non-LRTIs). Patients suspected of having LRTIs are retrospectively enrolled and a clinical metatranscriptome test is performed on bronchoalveolar lavage fluid (BALF). Transcriptomic and metagenomic analysis profiled the host response and respiratory microbiome in patients with confirmed LRTI (n = 126) or non-LRTIs (n = 75). Patients with evidenced LRTIs exhibited enhanced pathways on chemokine and cytokine response, neutrophile recruitment and activation, along with specific gene modules linked to LRTIs status and key blood markers. Moreover, LRTIs patients exhibited reduced diversity and evenness in the lower respiratory microbiome, likely driven by an increased abundance of bacterial pathogens. Host marker genes are selected, and classifiers are developed to distinguish patients with LRTIs, non-LRTIs, and indeterminate status, achieving an area under the receiver operating characteristic curve of 0.80 to 0.86 and validated in a subsequently enrolled cohort. Incorporating respiratory microbiome features further enhanced the classifier's performance. In summary, a single metatranscriptome test of BALF proved detailed profiles of host response and respiratory microbiome, enabling accurate LRTIs diagnosis.

Large language model for horizontal transfer of resistance gene: From resistance gene prevalence detection to plasmid conjugation rate evaluation

The burgeoning issue of plasmid-mediated resistance genes (ARGs) dissemination poses a significant threat to environmental integrity. However, the prediction of ARGs prevalence is overlooked, especially for emerging ARGs that are potentially evolving gene exchange hotspot. Here, we explored to classify plasmid or chromosome sequences and detect resistance gene prevalence by using DNABERT. Initially, the DNABERT fine-tuned in plasmid and chromosome sequences followed by multilayer perceptron (MLP) classifier could achieve 0.764 AUC (Area under curve) on external datasets across 23 genera, outperforming 0.02 AUC than traditional statistic-based model. Furthermore, Escherichia, Pseudomonas single genera based model were also be trained to explore its predict performance to ARGs prevalence detection. By integrating K-mer frequency attributes, our model could boost the performance to predict the prevalence of ARGs in an external dataset in Escherichia with 0.0281-0.0615 AUC and Pseudomonas with 0.0196-0.0928 AUC. Finally, we established a random forest model aimed at forecasting the relative conjugation transfer rate of plasmids with 0.7956 AUC, drawing on data from existing literature. It identifies the plasmid's repression status, cellular density, and temperature as the most important factors influencing transfer frequency. With these two models combined, they provide useful reference for quick and low-cost integrated evaluation of resistance gene transfer, accelerating the process of computer-assisted quantitative risk assessment of ARGs transfer in environmental field.

Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI)

A large volume of data is being captured through the Phasor Measurement Unit (PMU), which opens new opportunities and challenges to the study of transmission line faults. To be specific, the Phasor Measurement Unit (PMU) data represents many different states of the power networks. The states of the PMU device help to identify different types of transmission line faults. For a precise understanding of transmission line faults, only the parameters that contain voltage and current magnitude estimations are not sufficient. This requirement has been addressed by generating data with more parameters such as frequencies and phase angles utilizing the Phasor Measurement Unit (PMU) for data acquisition. The data has been generated through the simulation of a transmission line model on ePMU DSA tools and Matlab Simulink. Different machine learning models have been trained with the generated synthetic data to classify transmission line fault cases. The individual models including Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (K-NN) have outperformed other models in fault classification which have acquired a cross-validation accuracy of 99.84%, 99.83%, and 99.76% respectively across 10 folds. Soft voting has been used to combine the performance of these best-performing models. Accordingly, the constructed ensemble model has acquired a cross-validation accuracy of 99.88% across 10 folds. The performance of the combined models in the ensemble learning process has been analyzed through explainable AI (XAI) which increases the interpretability of the input parameters in terms of making predictions. Consequently, the developed model has been evaluated with several performance matrices, such as precision, recall, and f1 score, and also tested on the IEEE 14 bus system. To sum up, this article has demonstrated the classification of six scenarios including no fault and fault cases from transmission lines with a significant number of training parameters and also interpreted the effect of each parameter to make predictions of different fault cases with great success.