3篇
您的检索式：作者名="QIN Guimin"
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| 1 | Analysis of Commonly and Specifically Dysregulated Pathways in Three Women Cancers显示文摘Breast, ovarian and endometrial cancer are three most prevalent gynaecological malignancies. Identifying their common and specific biomarkers is significant for cancer prediction and therapy in females. We propose a method to identify dysregulated pathways in cancer through scoring pathways based on the molecular interaction data and genomic data. Commonly and specifically dysregulated pathways are analyzed across the above three female cancers, which have not been studied as a whole to the best of our knowledge. Our results demonstrate that all the three cancers have close relationships with Type II diabetes and cell cycle-related biology processes. Breast cancer is specifically related to immune system while ovarian cancer and endometrial cancer are associated with blood vascular-related systems such as renin-angiotensin system and coagulation system. In addition, dysregulated pathways are used to predict potential driver genes effectively according to their topological structure and biological information. | LIU Xiyang AN Yingying YU Baoguo QIN Guimin | 2018 | Chinese Journal of Electronics2018，27，5： | 4 |
| 2 | Spectral clustering for detecting protein complexes in protein-protein interaction ( PPI ) networks 显示文摘 | Qin Guimin Gao Lin | 2010 | Mathematical and Computer Modelling2010，52，1112： | 1 |
| 3 | AF-Net:A Medical Image Segmentation Network Based on Attention Mechanism and Feature Fusion显示文摘Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges,we propose a medical image segmentation network(AF-Net)based on attention mechanism and feature fusion,which can effectively capture global information while focusing the network on the object area.In this approach,we add dual attention blocks(DA-block)to the backbone network,which comprises parallel channels and spatial attention branches,to adaptively calibrate and weigh features.Secondly,the multi-scale feature fusion block(MFF-block)is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally,to restore the locations and shapes of organs,we adopt the global feature fusion blocks(GFF-block)to fuse high-level and low-level information,which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset),and the experimental results achieve 94.0%in mIoU and 96.3%in DICE,showing that our approach performs better than U-Net and other state-of-art methods. | Guimin Hou Jiaohua Qin Xuyu Xiang Yun Tan Neal N.Xiong | 2021 | Computers, Materials & Continua2021，，11： | 0 |
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