scholarly journals An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues

2013 ◽  
Vol 29 (16) ◽  
pp. 2032-2040 ◽  
Author(s):  
Ying-Ying Xu ◽  
Fan Yang ◽  
Yang Zhang ◽  
Hong-Bin Shen
Keyword(s):  
Subcellular Localization ◽  
Human Protein ◽  
Protein Subcellular Localization ◽  
Localization Predictor ◽  
Cancer Tissues

Prediction of human protein subcellular localization using deep learning

2018 ◽  
Vol 117 ◽  
pp. 212-217 ◽  
Author(s):  
Leyi Wei ◽  
Yijie Ding ◽  
Ran Su ◽  
Jijun Tang ◽  
Quan Zou
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
Human Protein ◽  
Protein Subcellular Localization

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.

scholarly journals Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Xiaotong Guo ◽  
Fulin Liu ◽  
Ying Ju ◽  
Zhen Wang ◽  
Chunyu Wang
Keyword(s):  
Subcellular Localization ◽  
Ensemble Classifier ◽  
Human Protein ◽  
Protein Subcellular Localization

Critical evaluation of web-based prediction tools for human protein subcellular localization

2019 ◽  
Vol 21 (5) ◽  
pp. 1628-1640 ◽  
Author(s):  
Yinan Shen ◽  
Yijie Ding ◽  
Jijun Tang ◽  
Quan Zou ◽  
Fei Guo
Keyword(s):  
Subcellular Localization ◽  
Critical Evaluation ◽  
Human Protein ◽  
Data Sets ◽  
Protein Subcellular Localization ◽  
Data Set ◽  
Prediction Tools ◽  
Benchmark Data ◽  
Subcellular Localization Prediction ◽  
Localization Prediction

Abstract Human protein subcellular localization has an important research value in biological processes, also in elucidating protein functions and identifying drug targets. Over the past decade, a number of protein subcellular localization prediction tools have been designed and made freely available online. The purpose of this paper is to summarize the progress of research on the subcellular localization of human proteins in recent years, including commonly used data sets proposed by the predecessors and the performance of all selected prediction tools against the same benchmark data set. We carry out a systematic evaluation of several publicly available subcellular localization prediction methods on various benchmark data sets. Among them, we find that mLASSO-Hum and pLoc-mHum provide a statistically significant improvement in performance, as measured by the value of accuracy, relative to the other methods. Meanwhile, we build a new data set using the latest version of Uniprot database and construct a new GO-based prediction method HumLoc-LBCI in this paper. Then, we test all selected prediction tools on the new data set. Finally, we discuss the possible development directions of human protein subcellular localization. Availability: The codes and data are available from http://www.lbci.cn/syn/.

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