154: Integration of TPSA and High-Throughput Mass Spectrometry Data Improves Prostate Cancer Prediction

2007 ◽  
Vol 177 (4S) ◽  
pp. 52-53
Author(s):  
Stefano Ongarello ◽  
Eberhard Steiner ◽  
Regina Achleitner ◽  
Isabel Feuerstein ◽  
Birgit Stenzel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data ◽  
Cancer Prediction

509 INTEGRATION OF TPSAAND HIGH-THROUGHPUT MASS SPECTROMETRY DATA IMPROVES PROSTATE CANCER PREDICTION

2007 ◽  
Vol 6 (2) ◽  
pp. 150
Author(s):  
S. Ongarello ◽  
E. Steiner ◽  
I. Feuerstein ◽  
R. Achleitner ◽  
B. Stenzel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data ◽  
Cancer Prediction

Detecting outlier peptides in quantitative high-throughput mass spectrometry data

2012 ◽  
Vol 75 (11) ◽  
pp. 3230-3239 ◽  
Author(s):  
Florian Erhard ◽  
Ralf Zimmer
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data

NORMALIZATION REGARDING NON-RANDOM MISSING VALUES IN HIGH-THROUGHPUT MASS SPECTROMETRY DATA

2005 ◽  
Author(s):  
PEI WANG ◽  
HUA TANG ◽  
HEIDI ZHANG ◽  
JEFFREY WHITEAKER ◽  
AMANDA G PAULOVICH ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
Missing Values ◽  
Mass Spectrometry Data

scholarly journals Evaluation and validation of an analytical approach for high-throughput metabolomic fingerprinting using direct introduction–high-resolution mass spectrometry: Applicability to classification of urine of scrapie-infected ewes

2018 ◽  
Vol 25 (2) ◽  
pp. 251-258 ◽  
Author(s):  
Estelle Rathahao-Paris ◽  
Sandra Alves ◽  
Nawel Boussaid ◽  
Nicole Picard-Hagen ◽  
Véronique Gayrard ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Throughput ◽  
Biological Samples ◽  
Matrix Effects ◽  
Mass Measurement ◽  
Direct Injection ◽  
Mass Spectrometry Data ◽  
Direct Injection Mass Spectrometry

Direct injection–mass spectrometry can be used to perform high-throughput metabolomic fingerprinting. This work aims to evaluate a global analytical workflow in terms of sample preparation (urine sample dilution), high-resolution detection (quality of generated data based on criteria such as mass measurement accuracy and detection sensitivity) and data analysis using dedicated bioinformatics tools. Investigation was performed on a large number of biological samples collected from sheep infected or not with scrapie. Direct injection–mass spectrometry approach is usually affected by matrix effects, eventually hampering detection of some relevant biomarkers. Reference compounds were spiked in biological samples to help evaluate the quality of direct injection–mass spectrometry data produced by Fourier Transform mass spectrometry. Despite the potential of high-resolution detection, some drawbacks still remain. The most critical is the presence of matrix effects, which could be minimized by optimizing the sample dilution factor. The data quality in terms of mass measurement accuracy and reproducible intensity was evaluated. Good repeatability was obtained for the chosen dilution factor (i.e., 2000). More than 150 analyses were performed in less than 16 hours using the optimized direct injection–mass spectrometry approach. Discrimination of different status of sheeps in relation to scrapie infection (i.e., scrapie-affected, preclinical scrapie or healthy) was obtained from the application of Shrinkage Discriminant Analysis to the direct injection–mass spectrometry data. The most relevant variables related to this discrimination were selected and annotated. This study demonstrated that the choice of appropriated dilution faction is indispensable for producing quality and informative direct injection–mass spectrometry data. Successful application of direct injection–mass spectrometry approach for high throughput analysis of a large number of biological samples constitutes the proof of the concept.

Interpretation of mass spectrometry data for high-throughput proteomics

2003 ◽  
Vol 376 (7) ◽  
pp. 1014-1022 ◽  
Author(s):  
Daniel C. Chamrad ◽  
Gerhard Koerting ◽  
Johan Gobom ◽  
Herbert Thiele ◽  
Joachim Klose ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data

Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies

2016 ◽  
Vol 15 (5) ◽  
Author(s):  
Alexia Kakourou ◽  
Werner Vach ◽  
Simone Nicolardi ◽  
Yuri van der Burgt ◽  
Bart Mertens
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
Biomarker Discovery ◽  
Protein Profiling ◽  
Mass Spectrometry Data ◽  
Clinical Proteomics ◽  
Intensity Level ◽  
Data Set ◽  
Fticr Mass Spectrometry ◽  
Mass Spectrometry Technology

AbstractMass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.

scholarly journals Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data

2005 ◽  
Vol 21 (10) ◽  
pp. 2200-2209 ◽  
Author(s):  
J. S. Yu ◽  
S. Ongarello ◽  
R. Fiedler ◽  
X. W. Chen ◽  
G. Toffolo ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Ovarian Cancer ◽  
Dimensionality Reduction ◽  
High Throughput ◽  
Mass Spectrometry Data
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