Multivariate classification of disease phenotypes of esophageal adenocarcinoma by pattern recognition analysis of MALDI-TOF mass spectra of serum N-linked glycans

2017 ◽  
Vol 132 ◽  
pp. 83-88 ◽  
Author(s):  
Barry K. Lavine ◽  
Collin G. White ◽  
Lin DeNoyer ◽  
Yehia Mechref
Keyword(s):  
Pattern Recognition ◽  
Esophageal Adenocarcinoma ◽  
Mass Spectra ◽  
Multivariate Classification ◽  
Disease Phenotypes ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra ◽  
Pattern Recognition Analysis

scholarly journals Network Analysis Based on Unique Spectral Features Enables an Efficient Selection of Genomically Diverse Operational Isolation Units

2021 ◽  
Vol 9 (2) ◽  
pp. 416
Author(s):  
Charles Dumolin ◽  
Charlotte Peeters ◽  
Evelien De Canck ◽  
Nico Boon ◽  
Peter Vandamme
Keyword(s):  
Network Analysis ◽  
Hierarchical Clustering ◽  
Mass Spectra ◽  
Spectral Features ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra ◽  
Diversity Studies ◽  
Technical Sample ◽  
Efficient Selection ◽  
Selection Of

Culturomics-based bacterial diversity studies benefit from the implementation of MALDI-TOF MS to remove genomically redundant isolates from isolate collections. We previously introduced SPeDE, a novel tool designed to dereplicate spectral datasets at an infraspecific level into operational isolation units (OIUs) based on unique spectral features. However, biological and technical variation may result in methodology-induced differences in MALDI-TOF mass spectra and hence provoke the detection of genomically redundant OIUs. In the present study, we used three datasets to analyze to which extent hierarchical clustering and network analysis allowed to eliminate redundant OIUs obtained through biological and technical sample variation and to describe the diversity within a set of spectra obtained from 134 unknown soil isolates. Overall, network analysis based on unique spectral features in MALDI-TOF mass spectra enabled a superior selection of genomically diverse OIUs compared to hierarchical clustering analysis and provided a better understanding of the inter-OIU relationships.

scholarly journals Incubation with Cu(II) and Zn(II) salts enhances MALDI-TOF mass spectra of amyloid-beta and α-synuclein toward in vivo analysis

2017 ◽  
Vol 53 (2) ◽  
pp. 162-171 ◽  
Author(s):  
Andrea R. Kelley ◽  
Madeline E. Colley ◽  
George Perry ◽  
Stephan B.H. Bach
Keyword(s):  
Amyloid Beta ◽  
Mass Spectra ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra ◽  
In Vivo Analysis

Quantitative Interpretation of MALDI-TOF Mass Spectra of Imperfect Carbosilane Dendrimers

2007 ◽  
Vol 79 (4) ◽  
pp. 1639-1645 ◽  
Author(s):  
Alena Krupková ◽  
Jan Čermák ◽  
Zuzana Walterová ◽  
Jiří Horský
Keyword(s):  
Mass Spectra ◽  
Quantitative Interpretation ◽  
Carbosilane Dendrimers ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra

Identification of Bacterial Proteins Observed in MALDI TOF Mass Spectra from Whole Cells

1999 ◽  
Vol 71 (15) ◽  
pp. 3226-3230 ◽  
Author(s):  
Ricky D. Holland ◽  
Christopher R. Duffy ◽  
Fatemeh Rafii ◽  
John B. Sutherland ◽  
Thomas M. Heinze ◽  
...  
Keyword(s):  
Mass Spectra ◽  
Whole Cells ◽  
Bacterial Proteins ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra

scholarly journals Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra

2007 ◽  
Vol 24 (1) ◽  
pp. 63-70 ◽  
Author(s):  
D. Mantini ◽  
F. Petrucci ◽  
P. Del Boccio ◽  
D. Pieragostino ◽  
M. Di Nicola ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Mass Spectra ◽  
Component Analysis ◽  
Independent Component ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra

Salmon Stock Identification Based on Elemental Composition of Vertebrae

1983 ◽  
Vol 40 (2) ◽  
pp. 215-229 ◽  
Author(s):  
T. J. Mulligan ◽  
L. Lapi ◽  
R. Kieser ◽  
S. B. Yamada ◽  
D. L. Duewer
Keyword(s):  
Pattern Recognition ◽  
Chemical Composition ◽  
Fluorescence Spectroscopy ◽  
Elemental Composition ◽  
Stock Identification ◽  
X Ray ◽  
Salmon Management ◽  
Salmon Stock ◽  
Pattern Recognition Analysis

The elemental chemical composition of salmon vertebrae has been measured by X-ray spectroscopy for two mixtures of stocks. Classification of fish to stock based on this composition has an accuracy in the range of 80–95%. Information on the techniques used, X-ray fluorescence spectroscopy and pattern recognition analysis, is presented for a reader with no background in these areas. The advantages and limitations of this method of stock identification are discussed.Key words: X-ray spectroscopy, pattern recognition analysis, stocks, salmon, management

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