Proteomics data validation: why all must provide data

2007 ◽  
Vol 3 (8) ◽  
pp. 518 ◽  
Author(s):  
Lennart Martens ◽  
Henning Hermjakob
Keyword(s):  
Data Validation ◽  
Proteomics Data

Added Value for Tandem Mass Spectrometry Shotgun Proteomics Data Validation through Isoelectric Focusing of Peptides

2005 ◽  
Vol 4 (6) ◽  
pp. 2273-2282 ◽  
Author(s):  
Manfred Heller ◽  
Mingliang Ye ◽  
Philippe E. Michel ◽  
Patrick Morier ◽  
Daniel Stalder ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Isoelectric Focusing ◽  
Shotgun Proteomics ◽  
Added Value ◽  
Tandem Mass ◽  
Data Validation ◽  
Proteomics Data

Recent Advances in MS-Based Plant Proteomics: Proteomics Data Validation Through Integration with Other Classic and -Omics Approaches

2019 ◽  
pp. 77-101 ◽  
Author(s):  
María-Dolores Rey ◽  
Luis Valledor ◽  
María Ángeles Castillejo ◽  
Rosa Sánchez-Lucas ◽  
Cristina López-Hidalgo ◽  
...  
Keyword(s):  
Data Validation ◽  
Proteomics Data ◽  
Plant Proteomics ◽  
Recent Advances

Blast Furnace Data Validation Using Deep Learning as an Enabler for Autonomous Blast Furnace

2020 ◽  
Author(s):  
A. Schmitz ◽  
F. Hansen ◽  
F. Giroldini ◽  
C. Schockaert
Keyword(s):  
Blast Furnace ◽  
Deep Learning ◽  
Data Validation

scholarly journals Data Validation of the DLR Earth Sensing Imaging Spectrometer DESIS

2020 ◽  
Author(s):  
Uta Heiden ◽  
Kevin Alonso Gonzalez ◽  
Martin Bachmann ◽  
Kara Burch ◽  
Emiliano Carmona ◽  
...  
Keyword(s):  
Data Validation ◽  
Imaging Spectrometer

scholarly journals Cerebrospinal fluid proteome shows disrupted neuronal development in multiple sclerosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ellen F. Mosleth ◽  
Christian Alexander Vedeler ◽  
Kristian Hovde Liland ◽  
Anette McLeod ◽  
Gerd Haga Bringeland ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Cerebrospinal Fluid ◽  
Neural Development ◽  
Transforming Growth Factor Beta ◽  
Transforming Growth Factor ◽  
Early Stage ◽  
Retinol Binding Protein ◽  
Proteomics Data ◽  
Disease Heterogeneity ◽  
Low Levels

AbstractDespite intensive research, the aetiology of multiple sclerosis (MS) remains unknown. Cerebrospinal fluid proteomics has the potential to reveal mechanisms of MS pathogenesis, but analyses must account for disease heterogeneity. We previously reported explorative multivariate analysis by hierarchical clustering of proteomics data of MS patients and controls, which resulted in two groups of individuals. Grouping reflected increased levels of intrathecal inflammatory response proteins and decreased levels of proteins involved in neural development in one group relative to the other group. MS patients and controls were present in both groups. Here we reanalysed these data and we also reanalysed data from an independent cohort of patients diagnosed with clinically isolated syndrome (CIS), who have symptoms of MS without evidence of dissemination in space and/or time. Some, but not all, CIS patients had intrathecal inflammation. The analyses reported here identified a common protein signature of MS/CIS that was not linked to elevated intrathecal inflammation. The signature included low levels of complement proteins, semaphorin-7A, reelin, neural cell adhesion molecules, inter-alpha-trypsin inhibitor heavy chain H2, transforming growth factor beta 1, follistatin-related protein 1, malate dehydrogenase 1 cytoplasmic, plasma retinol-binding protein, biotinidase, and transferrin, all known to play roles in neural development. Low levels of these proteins suggest that MS/CIS patients suffer from abnormally low oxidative capacity that results in disrupted neural development from an early stage of the disease.

Studying the effects of correlation on protein selection in proteomics data

PROTEOMICS ◽  
2009 ◽  
Vol 9 (10) ◽  
pp. 2883-2887
Author(s):  
Savita Venkataramani ◽  
Dayanand N. Naik
Keyword(s):  
Proteomics Data ◽  
Protein Selection

scholarly journals Estimation of PWC gradients over the Kanto Plain using GPS data: Validation and possible meteorological implications

2000 ◽  
Vol 52 (11) ◽  
pp. 907-912 ◽  
Author(s):  
Kazumasa Aonashi ◽  
Yoshinori Shoji ◽  
Ryu-ichi Ichikawa ◽  
Hiroshi Hanado
Keyword(s):  
Gps Data ◽  
Data Validation
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