Deep Learning-Based Classification of Protein Subcellular Localization from Immunohistochemistry Images

2017 ◽  
Author(s):  
Jin-Xian Hu ◽  
Ying-Ying Xu ◽  
Yang-Yang ◽  
Hong-Bin Shen
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
Protein Subcellular Localization

scholarly journals DeepLoc: prediction of protein subcellular localization using deep learning

2017 ◽  
Vol 33 (24) ◽  
pp. 4049-4049 ◽  
Author(s):  
Jose Juan Almagro Armenteros ◽  
Casper Kaae Sønderby ◽  
Søren Kaae Sønderby ◽  
Henrik Nielsen ◽  
Ole Winther
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
Protein Subcellular Localization

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

scholarly journals Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning

2016 ◽  
Author(s):  
Tanel Pärnamaa ◽  
Leopold Parts
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Compartment ◽  
Cell Localization ◽  
Image Characteristics ◽  
Basic Image ◽  
Training Examples

High throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high throughput microscopy.

scholarly journals DeepLoc: prediction of protein subcellular localization using deep learning

2017 ◽  
Vol 33 (21) ◽  
pp. 3387-3395 ◽  
Author(s):  
José Juan Almagro Armenteros ◽  
Casper Kaae Sønderby ◽  
Søren Kaae Sønderby ◽  
Henrik Nielsen ◽  
Ole Winther
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
Protein Subcellular Localization

scholarly journals Prediction of protein subcellular localization using deep learning and data augmentation

2020 ◽  
Author(s):  
Majid Ghorbani Eftekhar
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
Drug Targets ◽  
Data Augmentation ◽  
Future Research ◽  
Protein Subcellular Localization ◽  
Reliable Prediction ◽  
Computational Tools ◽  
The Past ◽  
Machine Learning Model

AbstractIdentifying subcellular localization of protein is significant for understanding its molecular function. It provides valuable insights that can be of tremendous help to protein’s function research and the detection of potential cell surface/secreted drug targets. The prediction of protein subcellular localization using bioinformatics methods is an inexpensive option to experimentally approaches. Many computational tools have been built during the past two decades, however, producing reliable prediction has always been the challenge. In this study, a Deep learning (DL) technique is proposed to enhance the precision of the analytical engine of one of these tools called PSORTb v3.0. Its conventional SVM machine learning model was replaced by the state-of-the-art DL method (BiLSTM) and a Data augmentation measure (SeqGAN). As a result, the combination of BiLSTM and SeqGAN outperformed SVM by improving its precision from 57.4% to 75%. This method was applied on a dataset containing 8230 protein sequences, which was experimentally derived by Brinkman Lab. The presented model provides promising outcomes for the future research. The source code of the model is available at https://github.com/mgetech/SubLoc.

scholarly journals Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhen-Zhen Xue ◽  
Yanxia Wu ◽  
Qing-Zu Gao ◽  
Liang Zhao ◽  
Ying-Ying Xu
Keyword(s):  
Colon Cancer ◽  
Subcellular Localization ◽  
Subcellular Location ◽  
Human Colon ◽  
Automated Classification ◽  
Protein Subcellular Localization ◽  
Protein Biomarkers ◽  
Image Patches ◽  
Protein Subcellular Locations

Abstract Background Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied. Results In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations. Conclusions Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.

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