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Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server.

Aim and Objective: Lung nodule detection is critical in improving the five-year survival rate and reducing mortality for patients with lung cancer. Numerous methods based on convolutional neural networks (CNNs) have been proposed for lung nodule detection in computed tomography (CT) images. With the collaborative development of computer hardware technology, the detection accuracy and efficiency can still be improved. Materials and Methods: In this study, an automatic lung nodule detection method using CNNs with transfer learning is presented. We first compare three of the state-of-the-art convolutional neural network (CNN) models, namely, VGG16, VGG19 and ResNet50, to determine the most suitable model for lung nodule detection. We then utilize two different training strategies, namely, freezing layers and fine-tuning, to illustrate the effectiveness of transfer learning. Furthermore, the hyper-parameters of the CNN model such as optimizer, batch size and epoch are optimized. Results: Evaluated on the Lung Nodule Analysis 2016 (LUNA16) challenge, promising results with an accuracy of 96.86%, a precision of 91.10%, a sensitivity of 90.78%, a specificity of 98.13%, and an AUC of 99.37% are achieved. Conclusion: Compared with other works, state-of-the-art specificity is obtained, which demonstrates that the proposed method is effective and applicable to lung nodule detection.