“…Our system, neXtA(5), is a curation service composed of three main elements. …”
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“…To evaluate the performance of neXtA(5), we submitted requests and compared the neXtA(5) results with the manual curation. …”
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“…neXtProt (http://www.nextprot.org/) is a new human protein-centric knowledge platform. …”
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“…neXtProt (http://www.nextprot.org) is a human protein-centric knowledgebase developed at the SIB Swiss Institute of Bioinformatics. …”
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“…The neXtProt human protein knowledgebase (https://www.nextprot.org) continues to add new content and tools, with a focus on proteomics and genetic variation data. neXtProt now has proteomics data for over 85% of the human proteins, as well as new tools tailored to the proteomics community. …”
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“…The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. …”
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“…In this study, we proposed a modified ResNeXt model by embedding a new regularization technique to improve the classification task. …”
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“…We describe the analytic validation of NeXT Dx, a comprehensive genomic profiling assay to aid therapy and clinical trial selection for patients diagnosed with solid tumor cancers. …”
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“…SUMMARY: The neXtProt peptide uniqueness checker allows scientists to define which peptides can be used to validate the existence of human proteins, i.e. map uniquely versus multiply to human protein sequences taking into account isobaric substitutions, alternative splicing and single amino acid variants. …”
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“…The ConvNeXT three-category model was trained on the original and expanded datasets, and compared to the results of the VGG16, ResNet18, ResNet50, EfficientNetB7, and RegNet three-category models. …”
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“…The design of the attention block, Attention TurkerNeXt, draws inspiration from ConvNeXt, with an added shortcut operation to mitigate the vanishing gradient problem. …”
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“…In addition, the training time per epochs of the model is very less compared to VGG19, ResNet-50, ResNeXt. The proposed CResNeXt model’s binary classification (COVID-19 versus No-Finding) accuracy is observed to be 98.63% and 99.99% and multi-class classification (COVID-19, Pneumonia, and No-Finding) accuracy is observed to be 97.42% and 99.27% on the original and augmented datasets, respectively.…”
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“…The average recognition accuracy of the improved network model for soybean leaf diseases was 85.42% both higher than the six deep learning comparison models (ConvNeXt (66.41%), ResNet50 (72.22%), Swin Transformer (77.00%), MobileNetV3 (67.27%), ShuffleNetV2 (59.89%), and SqueezeNet (72.92%)), thus proving the effectiveness of the improved method.The model proposed in this paper was also tested on the grapevine leaf dataset, and the performance ability of the improved network model remained due to other common network models, and overall the proposed network model was very effective in leaf disease identification.…”
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“…The experimental results show that SE can effectively help ConvNeXt acquire images' color features. The model's accuracy in predicting the replacement rate of steel slag sand is 87.99%, which is better than the original ConvNeXt network and other standard convolutional neural networks. …”
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“…Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. …”
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“…To address the above two issues, in this paper, we designed an end-to-end 3D-ResNeXt network which adopts feature fusion and label smoothing strategy further. …”
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“…Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. …”
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“…In this paper, we propose a deep learning ensemble framework called VitCNX which combines Vision Transformer and ConvNeXt for COVID-19 CT image identification. We compared our proposed model VitCNX with EfficientNetV2, DenseNet, ResNet-50, and Swin-Transformer which are state-of-the-art deep learning models in the field of image classification, and two individual models which we used for the ensemble (Vision Transformer and ConvNeXt) in binary and three-classification experiments. …”
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