High Throughput Location Proteomics in Confocal Images from the Human Protein Atlas Using a Bag-of-Features Representation

2014 ◽  
pp. 77-82 ◽  
Author(s):  
Raúl Ramos-Pollán ◽  
John Arévalo ◽  
Ángel Cruz-Roa ◽  
Fabio González
Keyword(s):  
High Throughput ◽  
Human Protein ◽  
Bag Of Features ◽  
Location Proteomics ◽  
Human Protein Atlas ◽  
Confocal Images

scholarly journals Automated Analysis and Reannotation of Subcellular Locations in Confocal Images from the Human Protein Atlas

PLoS ONE ◽  
2012 ◽  
Vol 7 (11) ◽  
pp. e50514 ◽  
Author(s):  
Jieyue Li ◽  
Justin Y. Newberg ◽  
Mathias Uhlén ◽  
Emma Lundberg ◽  
Robert F. Murphy
Keyword(s):  
Automated Analysis ◽  
Human Protein ◽  
Human Protein Atlas ◽  
Confocal Images

Investigating the Applicability of Antibodies Generated within the Human Protein Atlas as Capture Agents in Immunoenrichment Coupled to Mass Spectrometry

2014 ◽  
Vol 13 (10) ◽  
pp. 4424-4435 ◽  
Author(s):  
Tove Boström ◽  
Henrik J. Johansson ◽  
Janne Lehtiö ◽  
Mathias Uhlén ◽  
Sophia Hober
Keyword(s):  
Mass Spectrometry ◽  
Human Protein ◽  
Human Protein Atlas

scholarly journals Antibodies for profiling the human proteome-The Human Protein Atlas as a resource for cancer research

PROTEOMICS ◽  
2012 ◽  
Vol 12 (13) ◽  
pp. 2067-2077 ◽  
Author(s):  
Anna Asplund ◽  
Per-Henrik D. Edqvist ◽  
Jochen M. Schwenk ◽  
Fredrik Pontén
Keyword(s):  
Cancer Research ◽  
Human Proteome ◽  
Human Protein ◽  
Human Protein Atlas

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.

scholarly journals Analysis of the Human Protein Atlas Image Classification competition

2019 ◽  
Vol 16 (12) ◽  
pp. 1254-1261 ◽  
Author(s):  
Wei Ouyang ◽  
Casper F. Winsnes ◽  
Martin Hjelmare ◽  
Anthony J. Cesnik ◽  
Lovisa Åkesson ◽  
...  
Keyword(s):  
Protein Localization ◽  
Human Protein ◽  
Training Techniques ◽  
Wide Range ◽  
Pattern Similarity ◽  
Human Protein Atlas ◽  
Imbalanced Classes ◽  
Image Collection ◽  
And Training

AbstractPinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.

Abstract 446: Systematic discovery of biomarkers for ovarian cancer by using the Human Protein Atlas database

2016 ◽  
Author(s):  
Jianxiang Shi ◽  
Hao Sun ◽  
Hongfei Zhang ◽  
Mengtao Xing ◽  
Jitian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Human Protein ◽  
Human Protein Atlas
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