scholarly journals A highly sensitive near-infrared fluorescent detection method to analyze signalling pathways by reverse-phase protein array

PROTEOMICS ◽  
2009 ◽  
Vol 9 (24) ◽  
pp. 5446-5454 ◽  
Author(s):  
Laurence Dupuy ◽  
Christophe Gauthier ◽  
Guillaume Durand ◽  
Astrid Musnier ◽  
Domitille Heitzler ◽  
...  
Keyword(s):  
Near Infrared ◽  
Detection Method ◽  
Protein Array ◽  
Fluorescent Detection ◽  
Signalling Pathways ◽  
Reverse Phase Protein Array ◽  
Reverse Phase ◽  
Highly Sensitive ◽  
Phase Protein

Reconstruction of Protein Networks Using Reverse-Phase Protein Array Data

2016 ◽  
pp. 227-246
Author(s):  
Silvia von der Heyde ◽  
Johanna Sonntag ◽  
Frank Kramer ◽  
Christian Bender ◽  
Ulrike Korf ◽  
...  
Keyword(s):  
Protein Array ◽  
Protein Networks ◽  
Reverse Phase Protein Array ◽  
Reverse Phase ◽  
Array Data ◽  
Phase Protein

scholarly journals NormaCurve: A SuperCurve-Based Method That Simultaneously Quantifies and Normalizes Reverse Phase Protein Array Data

PLoS ONE ◽  
2012 ◽  
Vol 7 (6) ◽  
pp. e38686 ◽  
Author(s):  
Sylvie Troncale ◽  
Aurélie Barbet ◽  
Lamine Coulibaly ◽  
Emilie Henry ◽  
Beilei He ◽  
...  
Keyword(s):  
Protein Array ◽  
Reverse Phase Protein Array ◽  
Reverse Phase ◽  
Array Data ◽  
Phase Protein

scholarly journals Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery

Microarrays ◽  
2015 ◽  
Vol 4 (4) ◽  
pp. 520-539 ◽  
Author(s):  
Astrid Wachter ◽  
Stephan Bernhardt ◽  
Tim Beissbarth ◽  
Ulrike Korf
Keyword(s):  
Experimental Design ◽  
Biomarker Discovery ◽  
Protein Array ◽  
Reverse Phase Protein Array ◽  
Reverse Phase ◽  
Array Data ◽  
Phase Protein

635 NormaCurve, a New Tool for the Normalization of Reverse Phase Protein Array Data

2012 ◽  
Vol 48 ◽  
pp. S150 ◽  
Author(s):  
L. De Koning ◽  
S. Troncale ◽  
A. Barbet ◽  
L. Coulibaly ◽  
E. Henry ◽  
...  
Keyword(s):  
Protein Array ◽  
Reverse Phase Protein Array ◽  
Reverse Phase ◽  
Array Data ◽  
Phase Protein

scholarly journals Reverse-Phase Protein Array: Technology, Application, Data Processing, and Integration

2021 ◽  
pp. jbt.2021-3202-001
Author(s):  
Cristian Coarfa ◽  
Sandra L. Grimm ◽  
Kimal Rajapakshe ◽  
Dimuthu Perera ◽  
Hsin-Yi Lu ◽  
...  
Keyword(s):  
Data Processing ◽  
Protein Array ◽  
Reverse Phase Protein Array ◽  
Reverse Phase ◽  
Technology Application ◽  
Array Technology ◽  
Phase Protein ◽  
Application Data

scholarly journals Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Biomolecules ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1460 ◽  
Author(s):  
Satoshi Takahashi ◽  
Ken Asada ◽  
Ken Takasawa ◽  
Ryo Shimoyama ◽  
Akira Sakai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Protein Array ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Survival Prediction ◽  
Reverse Phase Protein Array ◽  
Reverse Phase ◽  
Log Rank Test ◽  
Omics Analysis ◽  
Phase Protein

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

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