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Journal of Healthcare Informatics Research

, Volume 2, Issue 3, pp 305–318 | Cite as

On Comprehensive Mass Spectrometry Data Analysis for Proteome Profiling of Human Blood Samples

  • Sameer Manchanda
  • Mikaela Meyer
  • Qianqian Li
  • Kai Liang
  • Yan Li
  • Nan KongEmail author
Research Article
Part of the following topical collections:
  1. Special Issue on Data Mining in Healthcare Informatics

Abstract

To guarantee meaningful interpretation of data in basic and translational medicine, it is critical to ensure the quality of biological samples. Mass spectrometers have become promising instruments to acquire proteomic information that is known to be associated with the quality of samples. However, a universally applicable mass spectrometry data analysis platform for quality assessment remains of great need. We present a comprehensive pattern recognition study to facilitate the development of such a platform. This study involves feature extraction, binary classification, and feature ranking. In this study, we develop classifiers with classification accuracy higher than 90% in distinguishing human serum samples stored for different amounts of time. We also derive fingerprint patterns of serum peptides that can be conveniently used for temporal classification.

Keywords

Proteome profiling Mass spectrometry Blood sample Binary classification Feature ranking 

Notes

Funding Information

This study received financial support from NSF grant DMS#1246818 and an industry grant from the Chinese Academy of Sciences Holding Co., Ltd.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer SciencePurdue UniversityWest LafayetteUSA
  2. 2.Department of Statistics and MathematicsPurdue UniversityWest LafayetteUSA
  3. 3.Institute of BiophysicsChinese Academy of SciencesBeijingChina
  4. 4.Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteUSA

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