On Comprehensive Mass Spectrometry Data Analysis for Proteome Profiling of Human Blood Samples
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.
KeywordsProteome profiling Mass spectrometry Blood sample Binary classification Feature ranking
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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