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Your search keyword '"Qin, Shengwei"' showing total 40 results
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40 results on '"Qin, Shengwei"'

23. GCSENet : A GCN, CNN and SENet ensemble model for microRNA-disease association prediction

Author

Li, Zhong, Jiang, Kaiyancheng, Qin, Shengwei, Zhong, Yijun, Elofsson, Arne, Li, Zhong, Jiang, Kaiyancheng, Qin, Shengwei, Zhong, Yijun, and Elofsson, Arne

Abstract

Recently, an increasing number of studies have demonstrated that miRNAs are involved in human diseases, indicating that miRNAs might be a potential pathogenic factor for various diseases. Therefore, figuring out the relationship between miRNAs and diseases plays a critical role in not only the development of new drugs, but also the formulation of individualized diagnosis and treatment. As the prediction of miRNA-disease association via biological experiments is expensive and time-consuming, computational methods have a positive effect on revealing the association. In this study, a novel prediction model integrating GCN, CNN and Squeeze-and-Excitation Networks (GCSENet) was constructed for the identification of miRNA-disease association. The model first captured features by GCN based on a heterogeneous graph including diseases, genes and miRNAs. Then, considering the different effects of genes on each type of miRNA and disease, as well as the different effects of the miRNA-gene and disease-gene relationships on miRNA-disease association, a feature weight was set and a combination of miRNA-gene and disease-gene associations was added as feature input for the convolution operation in CNN. Furthermore, the squeeze and excitation blocks of SENet were applied to determine the importance of each feature channel and enhance useful features by means of the attention mechanism, thus achieving a satisfactory prediction of miRNA-disease association. The proposed method was compared against other state-of-the-art methods. It achieved an AUROC score of 95.02% and an AUPR score of 95.55% in a 10-fold cross-validation, which led to the finding that the proposed method is superior to these popular methods on most of the performance evaluation indexes. Author summary Identifying miRNA-disease associations accelerates the understanding towards pathogenicity, which is beneficial for the development of treatment tools for diseases. Different from existing methods, our GCSENet captures t

Published
2021
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40. Effect of carburizing process on high cycle fatigue behavior of 18CrNiMo7-6 steel

Author

Qin, Shengwei, Zhang, Chenghao, Zhang, Bang, Ma, Haiyang, and Zhao, Minghao

Abstract

Carburizing heat treatment is widely used in the fabrication of gears to improve their fatigue performance. The effects of carburizing process on the material properties surface hardness, residual stresses, retained austenite of case-hardened 18CrNiMo7-6 and the consequential fatigue strength have not been studied in detail. In this research, the high-cycle fatigue performance of uncarburized and carburized 18CrNiMo7-6 gear steel were studied. In addition, the evolution processes of hardness, microstructure and residual stress of carburized specimens during the fatigue test were observed quasi-in situ. The initiation of fatigue cracks was also analyzed by SEM and the transformation of retained austenite during the fatigue test was observed by TEM. The results showed that the fatigue strength of carburized specimens has an increase-decrease trend with the increase of effective case depth. Besides, the fatigue failure mode of steel changes from surface failure to internal failure after being carburized, during which retained austenite plays an important part. In addition, if a large amount of retained austenite exists in the specimen with internal failure, the high-density twin martensite region formed by the retained austenite transformation can act as the crack initiation site, which is harmful to fatigue performance.

Published
2021
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