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Accession Number ADA586540
Title Science of Decision Making: A Data-Modeling Approach.
Publication Date Oct 2013
Media Count 20p
Personal Author R. E. Jabbour S. V. Deshpande
Abstract We have developed a parallel data analysis algorithm for peptide classification, which is used for microbial identification. This algorithm was based on data generated from the commercially available algorithms SEQUEST and OMSSA. The outputs from those algorithms were analyzed to determine a probability score for the identified peptides and their associated proteins. The statistical analyses and data interpretation using our proposed approach showed that we can lower the false-discovery rate by using common proteins from both algorithms. This approach showed that the identification accuracy and reliable classification of microbes were improved without increasing the data analysis time. In summary, we have a higher confidence in the identification process and a reduced bottleneck in data analysis through the use of the new algorithm.
Keywords Accuracy
Confidence level
Data processing
Identification
Microorganisms
Parallel processing
Peptides
Reliability
Statistical analysis

 
Source Agency Non Paid ADAS
NTIS Subject Category 57Y - Toxicology
72B - Algebra, Analysis, Geometry, & Mathematical Logic
57B - Biochemistry
Corporate Author Edgewood Chemical Biological Center, Aberdeen Proving Ground, MD. Research and Technology Directorate.
Document Type Technical report
Title Note Final rept. Sep 2011-Oct 2012.
NTIS Issue Number 1405
Contract Number N/A

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