ABSTRACT
The crisis of antibiotic resistance has become a significant global threat to human health. Understanding properties of antibiotic resistance genes (ARGs) is the first step to mitigate this issue. Although many methods have been proposed for predicting properties of ARGs, most of these methods focus only on predicting antibiotic classes, while ignoring other properties of ARGs, such as resistance mechanisms and transferability. However, acquiring all of these properties of ARGs can help researchers gain a more comprehensive understanding of the essence of antibiotic resistance, which will facilitate the development of antibiotics. In this paper, the task of predicting properties of ARGs is modeled as a multi-task learning problem, and an effective subtask-aware representation learning-based framework is proposed accordingly. More specifically, property-specific expert networks and shared expert networks are utilized respectively to learn subtask-specific features for each subtask and shared features among different subtasks. In addition, a gating-controlled mechanism is employed to dynamically allocate weights to subtask-specific semantics and shared semantics obtained respectively from property-specific expert networks and shared expert networks, thus adjusting distinctive contributions of subtask-specific features and shared features to achieve optimal performance for each subtask simultaneously. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs properties prediction. The data and source codes are available in GitHub at https://github.com/David-WZhao/GCM-ARG.
ABSTRACT
MOTIVATION: The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance. RESULTS: In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs. AVAILABILITY AND IMPLEMENTATION: The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.
Subject(s)
Anti-Bacterial Agents , Genes, Bacterial , Anti-Bacterial Agents/pharmacology , Drug Resistance, Microbial/genetics , SoftwareABSTRACT
BACKGROUND: Spinal gout (SG) is a rare condition. So far, a limited number of cases have been reported. Herein, we reported a single case of a 42-year-old male patient with SG involving the cervicothoracic and lumbar spine who underwent cervicothoracic segmental surgery. CASE SUMMARY: The patient presented to the hospital with neck pain and limb weakness lasting for one month. He had a history of gout for more than 10 years. Clinical and imaging findings indicated bone and joint tophus erosion, and the patient underwent standard tophi excision and internal fixation with a nail-and-rod system. Histopathological examination suggested gout-like lesions. After the operation, the patient's spinal nerve symptoms disappeared, and muscle strength gradually returned to normal. The patient maintained a low-purine diet and was recommended to engage in healthy exercises. The patient recovered well. CONCLUSION: Clinicians should highly suspect SG when patients with chronic gout presented with low back pain and neurological symptoms. Early decompression and debridement surgery are important to relieve neurological symptoms and prevent severe secondary neurological deficits.
ABSTRACT
Background: More than 150 types of brain tumors have been documented. Accurate diagnosis is important for making appropriate therapeutic decisions in treating the diseases. The goal of this study is to develop a DNA methylation profile-based classifier to accurately identify various kinds of brain tumors. Methods: Thirteen datasets of DNA methylation profiles were downloaded from the Gene Expression Omnibus (GEO) database, of which GSE90496 and GSE109379 were used as the training set and the validation set, respectively, and the remaining 11 sets were used as the independent test set. The random forest algorithm was used to select the CpG sites based on the importance of the features and a multilayer perceptron (MLP) model was trained to classify the samples. Deconvolution with the debCAM package was used to explore the cellular composition difference among tumors. Results: From training datasets with 2,801 samples, 396,568 CpG sites were retained after preprocessing, of which 767 were selected as the modeling features. A three-layer MLP model was developed, which consists of 1,320 nodes in the hidden layer, to predict the histological types of brain tumors. The prediction accuracy is 99.2, 87.0, and 96.58%, respectively, on the training, validation and test sets. The results of deconvolution analysis showed that the cell proportions of different tumor subtypes were different, and it is approximately enough to distinguish different tumor entities. Conclusion: We developed a classifier that is robust for the classification of central nervous system tumors, and tried to analyze the reasons for the classification performance.
