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

ZHANG Pingyue; ZHANG Mengtian; LIU Hui; YANG Yang

doi:10.1049/cje.2020.00.330

Volume 31 Issue 5

Sep. 2022

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2022 > 31(5): 888-896

ZHANG Pingyue, ZHANG Mengtian, LIU Hui, et al., “Prediction of Protein Subcellular Localization Based on Microscopic Images via Multi-Task Multi-Instance Learning,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 888-896, 2022, doi: 10.1049/cje.2020.00.330

Citation:

ZHANG Pingyue, ZHANG Mengtian, LIU Hui, et al., “Prediction of Protein Subcellular Localization Based on Microscopic Images via Multi-Task Multi-Instance Learning,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 888-896, 2022, doi: 10.1049/cje.2020.00.330

Citation:

PDF( 3492 KB)

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

doi: 10.1049/cje.2020.00.330

1.
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds: This work was supported by the National Natural Science Foundation of China (61972251)

More Information

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 (SJTU). 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 the Ph.D. degree in computer science in 2009 from SJTU. She was a Visiting Scholar in University of California, Riverside from 2012 to 2013. Currently, she is an Associate Professor of SJTU. Her research interests include machine learning and bioinformatics. (Email: yangyang@cs.sjtu.edu.cn)
Received Date: 2020-10-05
Accepted Date: 2021-12-30

Available Online: 2022-03-15

Publish Date: 2022-09-05

Abstract

Abstract

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.
- Microscopic image,
- Protein subcellular localization,
- Multi-instance learning,
- Multi-task learning,
- Deep neural network

FullText(HTML)

References(18)

References

[1]	K. Nakai and P. Horton, “PSORT: A program for detecting sorting signals in proteins and predicting their subcellular localization,” Trends in Biochemical Sciences, vol.24, no.1, pp.34–36, 1999. doi: 10.1016/S0968-0004(98)01336-X
[2]	X. Yang, X. Lei, and J. Zhao, “Essential protein prediction based on Shuffled frog-leaping algorithm,” Chinese Journal of Electronics, vol.30, no.4, pp.704–711, 2021. doi: 10.1049/cje.2021.05.012
[3]	X. Wang, Y. Cheng, and L. Li, “Protein function prediction based on active semi-supervised learning,” Chinese Journal of Electronics, vol.25, no.4, pp.595–600, 2016. doi: 10.1049/cje.2016.07.005
[4]	S. Briesemeister, J. Rahnenführer and O. Kohlbacher, “YLoc—An interpretable web server for predicting subcellular localization,” Nucleic Acids Research, vol.38, pp.W497–W502, 2010. doi: 10.1093/nar/gkq477
[5]	K. J. Park and M. Kanehisa, “Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs,” Bioinformatics, vol.19, no.13, pp.1656–1663, 2003. doi: 10.1093/bioinformatics/btg222
[6]	A. Pierleoni, P. L. Martelli, P. Fariselli, et al., “BaCelLo: A balanced subcellular localization predictor,” Bioinformatics, vol.22, no.14, pp.e408–e416, 2006. doi: 10.1093/bioinformatics/btl222
[7]	S. M. Chi and D. Nam, “WegoLoc: Accurate prediction of protein subcellular localization using weighted gene ontology terms,” Bioinformatics, vol.28, no.7, pp.1028–1030, 2012. doi: 10.1093/bioinformatics/bts062
[8]	H. Zhou, Y. Yang, and H. B. Shen, “Hum-mPLoc 3.0: Prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features,” Bioinformatics, vol.33, no.6, pp.843–853, 2017.
[9]	M. Uhlen, P. Oksvold, L. Fagerberg, et al., “Towards a knowledge-based human protein atlas,” Nature Biotechnology, vol.28, no.12, pp.1248–1250, 2010. doi: 10.1038/nbt1210-1248
[10]	Y. Y. Xu, H. B. Shen, and R. F. Murphy, “Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images,” Bioinformatics, vol.36, no.6, pp.1908–1914, 2020. doi: 10.1093/bioinformatics/btz844
[11]	W. Long, Y. Yang, and H. B. Shen, “ImPLoc: A multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images,” Bioinformatics, vol.36, no.7, pp.2244–2250, 2020. doi: 10.1093/bioinformatics/btz909
[12]	T. Peng, G. M. C. Bonamy, E. Glory-Afshar, et al., “Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns,” Proceedings of the National Academy of Sciences, vol.107, no.7, pp.2944–2949, 2010. doi: 10.1073/pnas.0912090107
[13]	J. X. Hu, Y. Yang, Y. Y. Xu, and H. B. Shen, “Incorporating label correlations into deep neural networks to classify protein subcellular location patterns in immunohistochemistry images,” Proteins: Structure, Function, and Bioinformatics, vol.90, no.2, pp.493–503, 2022. doi: 10.1002/prot.26244
[14]	J. R. R. Uijlings, K. E. A. Van De Sande, T. Gevers, et al., “Selective search for object recognition,” Int. J. of Computer Vision, vol.104, no.2, pp.154–171, 2013. doi: 10.1007/s11263-013-0620-5
[15]	K. He, X. Zhang, S. Ren, et al., “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.770–778, 2016.
[16]	G. Huang, Z. Liu, L. Van Der Maaten, et al., “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp.4700–4708, 2017.
[17]	A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint, arXiv: 1511.06434, 2015.
[18]	A. Vaswani, N. Shazeer, N. Parmar, et al., “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp.6000–6010, 2017.