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.

Advances in the Prediction of Protein Subcellular Locations with Machine Learning

2019 ◽  
Vol 14 (5) ◽  
pp. 406-421 ◽  
Author(s):  
Ting-He Zhang ◽  
Shao-Wu Zhang
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Protein Sequences ◽  
Subcellular Location ◽  
Automated Analysis ◽  
Cellular Level ◽  
Machine Learning Algorithms ◽  
Feature Representation ◽  
Protein Subcellular Location ◽  
Protein Subcellular Locations

Background: Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods. Methods: In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers. Result & Conclusion: Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

ImPLoc: a multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images

2019 ◽  
Vol 36 (7) ◽  
pp. 2244-2250 ◽  
Author(s):  
Wei Long ◽  
Yang Yang ◽  
Hong-Bin Shen
Keyword(s):  
Subcellular Localization ◽  
Tissue Level ◽  
Image Features ◽  
Supplementary Information ◽  
Protein Distribution ◽  
Protein Subcellular Localization ◽  
Significance Level ◽  
Protein Functions ◽  
Human Protein Atlas ◽  
Cancer Tissues

Abstract Motivation The tissue atlas of the human protein atlas (HPA) houses immunohistochemistry (IHC) images visualizing the protein distribution from the tissue level down to the cell level, which provide an important resource to study human spatial proteome. Especially, the protein subcellular localization patterns revealed by these images are helpful for understanding protein functions, and the differential localization analysis across normal and cancer tissues lead to new cancer biomarkers. However, computational tools for processing images in this database are highly underdeveloped. The recognition of the localization patterns suffers from the variation in image quality and the difficulty in detecting microscopic targets. Results We propose a deep multi-instance multi-label model, ImPLoc, to predict the subcellular locations from IHC images. In this model, we employ a deep convolutional neural network-based feature extractor to represent image features, and design a multi-head self-attention encoder to aggregate multiple feature vectors for subsequent prediction. We construct a benchmark dataset of 1186 proteins including 7855 images from HPA and 6 subcellular locations. The experimental results show that ImPLoc achieves significant enhancement on the prediction accuracy compared with the current computational methods. We further apply ImPLoc to a test set of 889 proteins with images from both normal and cancer tissues, and obtain 8 differentially localized proteins with a significance level of 0.05. Availability and implementation https://github.com/yl2019lw/ImPloc. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document