Professor | Ying-Ying Xu | Asia Growth Partners

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Name	Ying-Ying Xu
Contact Information	yyxu@smu.edu.cn
Language ability	English, Chinese
Province	Guangdong

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Name

Ying-Ying Xu

Contact Information

yyxu@smu.edu.cn

Language ability

English, Chinese

Province

Guangdong

University/Lab Affiliation and Title	Southern Medical University
Faculty	Biomedical Engineering
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
Work Experience	2017.07-present - Southern Medical University - Associate Professor

University/Lab Affiliation and Title

Southern Medical University

Faculty

Biomedical Engineering

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

Work Experience

2017.07-present - Southern Medical University - Associate Professor

Areas of Focus	Bioimage Informatics and Pattern Recognition
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

Areas of Focus

Bioimage Informatics and Pattern Recognition

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

Bioimage Informatics Pattern Recognition Deep Learning Protein Localization Immunofluorescence Subcellular Quantitative Analysis Computational Biology Machine Learning

Yang Bao - Southern Medical University

Yao Guo - Shanghai Jiao Tong University

Xiaohua Qian - Shanghai Jiao Tong University

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