Ying-Ying Xu

• Email: yyxu@smu.edu.cn
• Researchgate: https://www.researchgate.net/profile/Ying-Ying-Xu
• Github: https://yingying-xu.github.io
• Chinese webpage (中文): https://portal.smu.edu.cn/swyxgcxy/info/1020/1382.htm

About Me

My name is Ying-Ying Xu. I am an associate professor in the School of Biomedical Engineering, Southern Medical University. My research interests include bioinformatics, image processing, and pattern recognition. Our group develops machine learning models for the analysis of bioimages. For example, we design computational algorithms to recognize protein subcellular localization in both qualitative and quantitative manners from immunohistochemistry images, immunofluorescence images, and other proteomics data.

Experience

• Associate Professor, Southern Medical University, China (2017-)
• Visiting PhD Student, Carnegie Mellon University, USA, Supervised by Robert F. Murphy (2015-2016)
• PhD, Shanghai Jiao Tong University, China, Supervised by Hong-Bin Shen (2011-2017)
• BS, Northeastern University at Qinhuangdao, China (2007-2011)

Research Projects

• Interpretation of Protein Location Patterns from Microscope Images
• Prediction and Application of Protein Subcellular Localization Based on Multi-Source Data
• Object Detection and Classification for Medical Images

Publications

• Yu Li, Guo-Hua Zeng, Yong-Jia Liang, Hong-Rui Yang, Xi-Liang Zhu, Yu-Jia Zhai, Li-Xia Duan, and Ying-Ying Xu*. Improving quantitative prediction of protein subcellular locations in fluorescence images through deep generative models. Computers in Biology and Medicine, 2024, 179, 108913. https://doi.org/10.1016/j.compbiomed.2024.108913

• Xiaodu Yang, Dian He, Yu Li, Chenyang Li, Xinyue Wang, Xingzheng Zhu* , Haitao Sun* , and Ying-Ying Xu*. Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues. Biomedical Optics Express, 2024, 15(4):2498-2516. https://doi.org/10.1364/BOE.516541

• Lin-Xia Bao, Zhuo-Ming Luo, Xi-Liang Zhu, and Ying-Ying Xu*. Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images. Medical & Biological Engineering & Computing, 2023, 62:1105–1119. https://doi.org/10.1007/s11517-023-02985-x

• Dian He, Ting Li, Xiaodu Yang, Ying-Ying Xu, Haitao Sun*. Sudan Black B treatment for reducing autofluorescence in human glioma tissue and improving fluorescent signals of bacterial LPS staining. Journal of Biophotonics, 2023, e202200357. https://doi.org/10.1002/jbio.202200357

• Xi-Liang Zhu, Lin-Xia Bao, Min-Qi Xue, and Ying-Ying Xu*. Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning. Briefings in Bioinformatics, 2023, 24(1):bbac609. https://doi.org/10.1093/bib/bbac609

• Zhen-Zhen Xue, Cheng Li, Zhuo-Ming Luo, Shan-Shan Wang, and Ying-Ying Xu*. Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers. BMC Bioinformatics, 2022, 23:470. https://doi.org/10.1186/s12859-022-05015-z

• Jin-Xian Hu, Yang Yang, Ying-Ying Xu* , and Hong-Bin Shen*. GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images. Bioinformatics, 2022, 38(21):4941–4948. https://doi.org/10.1093/bioinformatics/btac634

• Xi-Liang Zhu, Hong-Bin Shen, Haitao Sun, Li-Xia Duan* , and Ying-Ying Xu*. Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery, 2022, 17(7):1303-1311. https://doi.org/10.1007/s11548-022-02587-2

• Ge Wang, Min-Qi Xue, Hong-Bin Shen* , and Ying-Ying Xu*. Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks. Briefings in Bioinformatics, 2022, 23(2):bbab539. https://doi.org/10.1093/bib/bbab539

• Min-Qi Xue, Xi-Liang Zhu, Ge Wang, and Ying-Ying Xu*. DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features. Bioinformatics, 2022, 38(3):827–833. https://doi.org/10.1093/bioinformatics/btab730

• Jin-Xian Hu, Yang Yang, Ying-Ying Xu* , and Hong-Bin Shen*. Incorporating label correlations into deep neural networks to classify protein subcellular location patterns in immunohistochemistry images. Proteins: Structure, Function, and Bioinformatics, 2022, 90(2):493-503. https://doi.org/10.1002/prot.26244

• Jiajia Zhao, Dian He, Hei Ming Lai, Ying-Ying Xu, Yunhao Luo, Ting Li, Jianhao Liang, Xiaodu Yang, Linlang Guo, Yiquan Ke, Hongwei Zhou, Wutian Wu* , Hongbo Guo* , Haitao Sun*. Comprehensive histological imaging of native microbiota in human glioma. Journal of Biophotonics, 2021, e202100351. https://doi.org/10.1002/jbio.202100351

• Ge Wang, Yu-Jia Zhai, Zhen-Zhen Xue, and Ying-Ying Xu*. Improving protein subcellular location classification by incorporating three-dimensional structure information. Biomolecules, 2021, 11(11):1607. https://doi.org/10.3390/biom11111607

• Ying-Ying Xu, Hang Zhou, Robert F. Murphy, and Hong-Bin Shen*. Consistency and variation of protein subcellular location annotations. Proteins: Structure, Function, and Bioinformatics, 2021, 89(2):242-250. https://doi.org/10.1002/prot.26010

• Zhen-Zhen Xue^, Yanxia Wu^, Qing-Zu Gao, Liang Zhao, and Ying-Ying Xu*. Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer. BMC Bioinformatics, 2020, 21(1):398. https://doi.org/10.1186/s12859-020-03731-y

• Ying-Ying Xu, Hong-Bin Shen* , and Robert F. Murphy*. Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images. Bioinformatics, 2020, 36(6):1908-1914. https://doi.org/10.1093/bioinformatics/btz844

• Ying-Ying Xu, Li-Xiu Yao, and Hong-Bin Shen*. Bioimage-based protein subcellular location prediction: a comprehensive review. Frontiers of Computer Science, 2018, 12(1):26-39. https://doi.org/10.1007/s11704-016-6309-5

• Wei Shao, Mingxia Liu, Ying-Ying Xu, Hong-Bin Shen, and Daoqiang Zhang. An organelle correlation-guided feature selection approach for classifying multi-label subcellular bioimages. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, 15(3):828-838. https://doi.org/10.1109/TCBB.2017.2677907

• Jin-Xian Hu, Ying-Ying Xu, Yang Yang, and Hong-Bin Shen*. Deep learning-based classification of protein subcellular localization from immunohistochemistry images. 4th IAPR Asian Conference on Pattern Recognition (ACPR 2017), 2017, Nanjing, China. https://doi.org/10.1109/ACPR.2017.125

• Ying-Ying Xu, Fan Yang, and Hong-Bin Shen*. Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction. Bioinformatics, 2016, 32(14):2184-2192. https://doi.org/10.1093/bioinformatics/btw219

• Xi Yin, Ying-Ying Xu, and Hong-Bin Shen*. Enhancing the prediction of transmembrane β-Barrel segments with chain learning and feature sparse representation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016, 13(6):1016-1026. https://doi.org/10.1109/TCBB.2016.2528000

• Ying-Ying Xu, Fan Yang, Yang Zhang, and Hong-Bin Shen*. Bioimaging based detection of mislocalized proteins in human cancers by semi-supervised learning. Bioinformatics, 2015, 31(7):1111-1119. https://doi.org/10.1093/bioinformatics/btu772

• Fan Yang, Ying-Ying Xu, Shi-Tong Wang, and Hong-Bin Shen*. Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing, 2014, 131:113-123. https://doi.org/10.1016/j.neucom.2013.10.034

• Fan Yang, Ying-Ying Xu, and Hong-Bin Shen*. Many local pattern texture features: which is better for image-based multi-label human protein subcellular localization classification?. The Scientific World Journal, 2014: 429049. https://doi.org/10.1155/2014/429049

• Ying-Ying Xu, Fan Yang, Yang Zhang* , and Hong-Bin Shen*. An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues. Bioinformatics, 2013, 29(16):2032-2040. https://doi.org/10.1093/bioinformatics/btt320

• Fan Yang, Ying-Ying Xu, and Hong-Bin Shen*. Automated classification of protein subcellular location patterns on images of human reproductive tissues. 3rd Sino-foreign-interchange workshop on Intelligent Science and Intelligent Data Engineering (IScIDE 2012), 2012, 254-262, Nanjing, China. https://doi.org/10.1007/978-3-642-36669-7_32