Incorporating label correlations into deep neural networks to classify protein subcellular location patterns in immunohistochemistry images

2021 ◽  
Author(s):  
Jin‐Xian Hu ◽  
Yang Yang ◽  
Ying‐Ying Xu ◽  
Hong‐Bin Shen
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Subcellular Location ◽  
Protein Subcellular Location ◽  
Location Patterns ◽  
Label Correlations

scholarly journals Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images

2019 ◽  
Vol 36 (6) ◽  
pp. 1908-1914 ◽  
Author(s):  
Ying-Ying Xu ◽  
Hong-Bin Shen ◽  
Robert F Murphy
Keyword(s):  
Subcellular Location ◽  
Distribution Patterns ◽  
Cell Types ◽  
Supplementary Information ◽  
Protein Distribution ◽  
Protein Subcellular Location ◽  
Location Patterns ◽  
Location Proteomics ◽  
Human Protein Atlas ◽  
Protein Subcellular Locations

Abstract Motivation Systematic and comprehensive analysis of protein subcellular location as a critical part of proteomics (‘location proteomics’) has been studied for many years, but annotating protein subcellular locations and understanding variation of the location patterns across various cell types and states is still challenging. Results In this work, we used immunohistochemistry images from the Human Protein Atlas as the source of subcellular location information, and built classification models for the complex protein spatial distribution in normal and cancerous tissues. The models can automatically estimate the fractions of protein in different subcellular locations, and can help to quantify the changes of protein distribution from normal to cancer tissues. In addition, we examined the extent to which different annotated protein pathways and complexes showed similarity in the locations of their member proteins, and then predicted new potential proteins for these networks. Availability and implementation The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/complexsubcellularpatterns. Supplementary information Supplementary data are available at Bioinformatics online.

Protein subcellular localization based on deep image features and criterion learning strategy

2020 ◽  
Author(s):  
Ran Su ◽  
Linlin He ◽  
Tianling Liu ◽  
Xiaofeng Liu ◽  
Leyi Wei
Keyword(s):  
Neural Networks ◽  
Subcellular Localization ◽  
Learning Strategy ◽  
Subcellular Location ◽  
Image Features ◽  
Protein Subcellular Localization ◽  
Protein Subcellular Location ◽  
Protein Functions ◽  
Deep Image ◽  
Criterion Learning

Abstract The spatial distribution of proteome at subcellular levels provides clues for protein functions, thus is important to human biology and medicine. Imaging-based methods are one of the most important approaches for predicting protein subcellular location. Although deep neural networks have shown impressive performance in a number of imaging tasks, its application to protein subcellular localization has not been sufficiently explored. In this study, we developed a deep imaging-based approach to localize the proteins at subcellular levels. Based on deep image features extracted from convolutional neural networks (CNNs), both single-label and multi-label locations can be accurately predicted. Particularly, the multi-label prediction is quite a challenging task. Here we developed a criterion learning strategy to exploit the label–attribute relevancy and label–label relevancy. A criterion that was used to determine the final label set was automatically obtained during the learning procedure. We concluded an optimal CNN architecture that could give the best results. Besides, experiments show that compared with the hand-crafted features, the deep features present more accurate prediction with less features. The implementation for the proposed method is available at https://github.com/RanSuLab/ProteinSubcellularLocation.

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