Science of Decision Making: A Data-Modeling Approach

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.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2013
Accession Number
ADA586540

Entities

People

  • Rabih E. Jabbour
  • Samir V. Deshpande

Organizations

  • Edgewood Chemical Biological Center

Tags

Communities of Interest

  • Autonomy
  • Counter WMD
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Bacteria
  • Classification
  • Data Analysis
  • Data Modeling
  • Data Science
  • Databases
  • Identification
  • Information Science
  • Liquid Chromatography
  • Mass Spectra
  • Mass Spectrometry
  • Microorganisms
  • Probability
  • Proteins
  • Spectra
  • Statistical Analysis

Readers

  • Computational Modeling and Simulation
  • Microbial Pathology
  • Neural Network Machine Learning.

Technology Areas

  • Biotechnology