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ZHANG Pingyue, ZHANG Mengtian, LIU Hui, YANG Yang. Prediction of Protein Subcellular Localization Based on Microscopic Images via Multi-Task Multi-Instance Learning[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.330
Citation: ZHANG Pingyue, ZHANG Mengtian, LIU Hui, YANG Yang. Prediction of Protein Subcellular Localization Based on Microscopic Images via Multi-Task Multi-Instance Learning[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.330

Prediction of Protein Subcellular Localization Based on Microscopic Images via Multi-Task Multi-Instance Learning

doi: 10.1049/cje.2020.00.330
Funds:  This work is supported by the National Natural Science Foundation of China (No. 61972251)
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  • Author Bio:

    is currently a master student in the Department of Computer Science and Engineering, Shanghai Jiao Tong University. He received the B.E. degree from Shanghai Jiao Tong University in 2021. His research interests include machine learning and self-supervised learning. (Email: williamzhangsjtu@sjtu.edu.cn)

    is currently a master student in the Department of Computer Science and Engineering, Shanghai Jiao Tong University. He received the B.E. degree from Shanghai Jiao Tong University in 2021. His research interests include computer vision and spatial-temporal data processing. (Email: zhangmengtian@sjtu.edu.cn)

    is currently a master student in the Department of Computer Science and Engineering, Shanghai Jiao Tong University. She received the B.E. degree from Shanghai Jiao Tong University in 2021. Her research interests include machine learning and security. (Email: sjtuliuhui@sjtu.edu.cn)

    (corresponding author) received her PhD in Computer Science in 2009 from Shanghai Jiao Tong University, China. She was a visiting scholar in University of California - Riverside from 2012 to 2013. Currently, she is an associate professor in the Department of Computer Science, Shanghai Jiao Tong University. Her research interests focus on machine learning and bioinformatics. (Email: yangyang@cs.sjtu.edu.cn)

  • Accepted Date: 2021-12-30
  • Available Online: 2022-03-15
  • Protein localization information is essential for understanding protein functions and their roles in various biological processes. The image-based prediction methods of protein subcellular localization have emerged in recent years because of the advantages of microscopic images in revealing spatial expression and distribution of proteins in cells. However, the image-based prediction is a very challenging task, due to the multi-instance nature of the task and low quality of images. In this paper, we propose a multi-task learning strategy and mask generation to enhance the prediction performance. Furthermore, we also investigate effective multi-instance learning schemes. We collect a large-scale dataset from the Human Protein Atlas database, and the experimental results show that the proposed multi-task multi-instance learning model outperforms both single-instance learning and common multi-instance learning methods by large margins.
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