Upload Avatar (500 x 500)
Ying-Ying Xu
Biomedical Engineering
Southern Medical University
Guangdong
Language: English, Chinese
Bioimage
Informatics
Pattern Recognition
Deep Learning
Protein Localization
Immunofluorescence
Subcellular
Quantitative Analysis
Computational Biology
Machine Learning
Areas of Focus
- Bioimage Informatics and Pattern Recognition
Work Experience
- 2017.07-present - Southern Medical University - Associate Professor
Academic Background & Achievements
- 2011.09-2017.06 PhD: Shanghai Jiao Tong University, Pattern Recognition and Intelligent Systems
- 2015.09-2016.09 Joint PhD Student: Carnegie Mellon University, Computational Biology
Publications
- Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning, Xi-Liang Zhu, Lin-Xia Bao, Min-Qi Xue, and Ying-Ying Xu, 2022
- Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers, Zhen-Zhen Xue, Cheng Li, Zhuo-Ming Luo, Shan-Shan Wang, and Ying-Ying Xu, 2022
- GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images, Jin-Xian Hu, Yang Yang, Ying-Ying Xu, and Hong-Bin Shen, 2022
- Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks, Xi-Liang Zhu, Hong-Bin Shen, Haitao Sun, Li-Xia Duan, and Ying-Ying Xu, 2022
- Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks, Ge Wang, Min-Qi Xue, Hong-Bin Shen, and Ying-Ying Xu, 2022
- DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features, Min-Qi Xue, Xi-Liang Zhu, Ge Wang, and Ying-Ying Xu, 2022
- Incorporating label correlations into deep neural networks to classify protein subcellular location patterns in immunohistochemistry images, Jin-Xian Hu, Yang Yang, Ying-Ying Xu, and Hong-Bin Shen, 2022
- Improving protein subcellular location classification by incorporating three-dimensional structure information, Ge Wang, Yu-Jia Zhai, Zhen-Zhen Xue, and Ying-Ying Xu, 2021
- Consistency and variation of protein subcellular location annotations, Ying-Ying Xu, Hang Zhou, Robert F. Murphy, and Hong-Bin Shen, 2021
- Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer, Zhen-Zhen Xue, Yanxia Wu, Qing-Zu Gao, Liang Zhao, Ying-Ying Xu, 2020
- Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images, Ying-Ying Xu, Hong-Bin Shen, Robert F. Murphy, 2020
- Bioimage-based protein subcellular location prediction: a comprehensive review, Ying-Ying Xu, Li-Xiu Yao, Hong-Bin Shen, 2018
- An organelle correlation-guided feature selection approach for classifying multi-label subcellular bioimages, Wei Shao, Ming-Xia Liu, Ying-Ying Xu, Hong-Bin Shen, Dao-Qiang Zhang, 2018
- Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction, Ying-Ying Xu, Fan Yang, Hong-Bin Shen, 2016
- Enhancing the prediction of transmembrane beta-barrel segments with chain learning and feature sparse representation, Xi Yin, Ying-Ying Xu, Hong-Bin Shen, 2016
- Bioimaging based detection of mislocalized proteins in human cancers by semi-supervised learning, Ying-Ying Xu, Fan Yang, Yang Zhang, Hong-Bin Shen, 2015
- Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features, Fan Yang, Ying-Ying Xu, Shi-Tong Wang, Hong-Bin Shen, 2014
- An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues, Ying-Ying Xu, Fan Yang, Yang Zhang, Hong-Bin Shen, 2013
