pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning

Shao, Yu-Tao; Chou, Kuo‐Chen

doi:10.4236/ns.2020.126033

Cited by 20 publications

(14 citation statements)

References 63 publications

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“…Eventually, they discovered that realtime spectroscopic forecasting could be performed on the structure of tiny organic molecules, and potential application molecules could be determined (Ghosh et al, 2019). Shao and Chou (2020) developed a CNN-based forecasting factor for the subcellular localization of viral proteins, called Ploc-Deep-mVirus, for the Coronavirus Disease 2019 (COVID-19) currently spreading globally. They found that this forecasting factor was particularly suitable for processing multi-site systems, and its forecasting performance was remarkably better than other advanced forecasting indicators (Shao and Chou, 2020).…”

Section: Recent Work ML Applicationsmentioning

confidence: 99%

“…Shao and Chou (2020) developed a CNN-based forecasting factor for the subcellular localization of viral proteins, called Ploc-Deep-mVirus, for the Coronavirus Disease 2019 (COVID-19) currently spreading globally. They found that this forecasting factor was particularly suitable for processing multi-site systems, and its forecasting performance was remarkably better than other advanced forecasting indicators (Shao and Chou, 2020). Ahmed et al (2020) applied DL approaches to the clinical or behavioral recognition of Autism Spectrum Disorder (ASD).…”

Section: Recent Work ML Applicationsmentioning

confidence: 99%

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Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

et al. 2021

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The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.

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Section: Recent Work ML Applicationsmentioning

confidence: 99%

Section: Recent Work ML Applicationsmentioning

confidence: 99%

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

et al. 2021

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“…A DL-based (CNN) model has been introduced to estimate the localization of sub-cellular viral proteins of SARS-CoV-2. This model's regional accuracy is above 98%, which can effectively discover various-target drugs for viral infections [ 53 ].…”

Section: Current Applications Of ML In Treatment Of the Sars-cov-2mentioning

confidence: 99%

Contribution of machine learning approaches in response to SARS-CoV-2 infection

Mottaqi¹,

Mohammadipanah²,

Sajedi³

2021

Informatics in Medicine Unlocked

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Problem The lately emerged SARS-CoV-2 infection, which has put the whole world in an aberrant demanding situation, has generated an urgent need for developing effective responses through artificial intelligence (AI). Aim This paper aims to overview the recent applications of machine learning techniques contributing to prevention, diagnosis, monitoring, and treatment of coronavirus disease (SARS-CoV-2). Methods A progressive investigation of the recent publications up to November 2020, related to AI approaches towards managing the challenges of COVID-19 infection was made. Results For patient diagnosis and screening, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are broadly applied for classification purposes. Moreover, Deep Neural Network (DNN) and homology modeling are the most used SARS-CoV-2 drug repurposing models. Conclusion While the fields of diagnosis of the SARS-CoV-2 infection by medical image processing and its dissemination pattern through machine learning have been sufficiently studied, some areas such as treatment outcome in patients and drug development need to be further investigated using AI approaches.

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“…Shao et al (2020) developed a CNN-based predictor of viral protein subcellular positioning for infectious diseases caused by a coronavirus, COVID-19, and H1N1 currently spreading around the world, called Ploc-Deep-mVirus. They found that this predictor was particularly suitable for processing multi-site systems, and its predictive performance was significantly better than that of advanced predictive indicators at present [ 18 ].…”

Section: Research On Dl’s Application Trendmentioning

confidence: 99%

Applying TS-DBN model into sports behavior recognition with deep learning approach

Guo

Wang

2021

J Supercomput

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The purposes are to automatically collect information about human sports behavior from massive video data and provide an explicit recognition and analysis of body movements. The analysis of multi-scale input data, the improvement of spatiotemporal Deep Belief Network (DBN), and the different pooling strategies are regarded as the focuses to improve the belief networks in deep learning (DL). Moreover, a human sports behavior recognition model is proposed based on particular spatio-temporal features. Also, video frame data are collected from the Royal Institute of Technology (KTH) and University of Central Florida (UCF) datasets for training. The TensorFlow platform is employed to simulate the built algorithm. Finally, the constructed algorithm model is compared with the DBN proposed by Yang et al. the Convolutional Neural Network (CNN) proposed by Ullah et al. and the DBN-Hidden Markov Model (HMM) algorithm proposed by Xu et al. to analyse its performance. The recognition effects of each algorithm in the two datasets are analyzed. Results demonstrate that CNN developed by Ullah et al. has the worst accuracy on the KTH and UCF datasets, followed by DBN developed by Yang et al. and DBN-HMM developed by Xu et al. The constructed algorithm model can provide the highest accuracy, reaching about 90%, and the recognition accuracy of human sports behaviors of each algorithm on the KTH dataset is lower than that on the UCF dataset. On the KTH dataset, the recognition accuracy for boxing is the highest and running the lowest. Analyzing the model’s accuracy in the four scenes (S1, S2, S3, and S4) on the KTH dataset suggests that the recognition accuracy for the indoor scene (S4) is higher than that of the outdoor scenes (S1, S2, and S3). On the UCF dataset, the recognition accuracy for lifting is the highest, reaching 99%, and that for walking is the lowest, reaching 51%. Therefore, the proposed human sports recognition model can provide a higher accuracy than other classic DL algorithms, providing an experimental basis for subsequent human sports recognition research.

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pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning

Cited by 20 publications

References 63 publications

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

Contribution of machine learning approaches in response to SARS-CoV-2 infection

Applying TS-DBN model into sports behavior recognition with deep learning approach

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