The Human Protein Atlas and Antibody-Based Tissue Profiling in Clinical Proteomics

2021 ◽  
pp. 191-206
Author(s):  
Borbala Katona ◽  
Cecilia Lindskog
Keyword(s):  
Human Protein ◽  
Clinical Proteomics ◽  
Tissue Profiling ◽  
Human Protein Atlas

scholarly journals Antibody-based tissue profiling as a tool for clinical proteomics

2004 ◽  
Vol 1 (3-4) ◽  
pp. 285-299 ◽  
Author(s):  
Caroline Kampf ◽  
Ann-Catrin Andersson ◽  
Kenneth Wester ◽  
Erik Björling ◽  
Mathias Uhlen ◽  
...  
Keyword(s):  
Clinical Proteomics ◽  
Tissue Profiling

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 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

Unraveling the human protein atlas of metastatic melanoma in the course of ultraviolet radiation-derived photo-therapy

2018 ◽  
Vol 188 ◽  
pp. 119-138 ◽  
Author(s):  
Eumorphia G. Konstantakou ◽  
Athanassios D. Velentzas ◽  
Athanasios K. Anagnostopoulos ◽  
Aikaterini F. Giannopoulou ◽  
Ema Anastasiadou ◽  
...  
Keyword(s):  
Ultraviolet Radiation ◽  
Metastatic Melanoma ◽  
Human Protein ◽  
Human Protein Atlas

The Human Protein Atlas—a tool for pathology

2008 ◽  
Vol 216 (4) ◽  
pp. 387-393 ◽  
Author(s):  
F Pontén ◽  
K Jirström ◽  
M Uhlen
Keyword(s):  
Human Protein ◽  
Human Protein Atlas
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