A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences

Deif, Mohanad A.; Solyman, Ahmed A. A.; Kamarposhti, Mehrdad Ahmadi; Band, Shahab S.; Hammam, Rania E.

doi:10.3934/mbe.2021440

Cited by 25 publications

(14 citation statements)

References 27 publications

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“…Commonly used metrics are employed in the proposed study to evaluate the performance and prediction errors of the proposed models. These metrics include the square of the correlation coefficient R 2 [59,60] and the RMSE [61]. The values of both metrics yield a good indication of the accuracy and prediction capability of the proposed models and allow us to select the most efficient model for the prediction of wear rates of UHMWPE.…”

Section: Multiobjective Grey Wolf Optimizermentioning

confidence: 99%

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Hammam

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

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One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness R a values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments.

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Section: Multiobjective Grey Wolf Optimizermentioning

confidence: 99%

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Hammam

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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“…The input is routed across the network layers during the forward phase when training a CNN [ 19 , 41 , 42 ]. Gradients are back-propagated and neuron weights are updated during the backward phase.…”

Section: Materials and Methodsmentioning

confidence: 99%

Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

Baghdadi

Maklad

Malki

et al. 2022

Sensors

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Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard’s and Macenko’s conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko’s approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use.

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“…Chen et al [ 20 ] and Zou et al [ 21 ] reported different ensemble methods based on the CNN and long short-term memory (LSTM) to predict the human N6-methyladenosine sites from mRNA sequences. Deif et al [ 22 ] came up with a deep bidirectional recurrent neural networks (BRNNs) model that combined with LSTM and GRU to distinguish between the genome sequence of SARS-CoV-2 and other coronavirus strains. Kumar et al [ 23 ] constructed two models for predicting COVID-19 by using modified LSTM and reinforcement learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in Chinese Pregnant Women: A Prospective Cohort Study

Liu

Zhan

Liu

et al. 2022

Journal of Healthcare Engineering

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Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner’s inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628–0.648)) and specificity (0.683 (0.669–0.698)) and lowest error rate (0.362 (0.352–0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663–0.670)) and specificity (0.725 (0.721–0.729)) and decreased the error rate (0.333 (0.330–0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.

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A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences

Cited by 25 publications

References 27 publications

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in Chinese Pregnant Women: A Prospective Cohort Study

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