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 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 Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization

2021 ◽  
Author(s):  
Hirofumi Kobayashi ◽  
Keith C Cheveralls ◽  
Manuel Leonetti ◽  
Loic Alain Royer
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Subcellular Localization ◽  
Protein Localization ◽  
Protein Subcellular Localization ◽  
Supervised Training ◽  
Cellular Architecture ◽  
Training Scheme ◽  
Emergent Features ◽  
Fluorescence Images

Elucidating the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here, we present cytoself, a deep learning-based approach for fully self-supervised protein localization profiling and clustering. cytoself leverages a self-supervised training scheme that does not require pre-existing knowledge, categories, or annotations. Applying cytoself to images of 1311 endogenously labeled proteins from the recently released OpenCell database creates a highly resolved protein localization atlas. We show that the representations derived from cytoself encapsulate highly specific features that can be used to derive functional insights for proteins on the sole basis of their localization. Finally, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret these features in the context of the fluorescence images, and analyze the performance contributions of the different components of our approach.

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