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

被引：0

作者：

Manchanda S. ^{[1
]}

Meyer M. ^{[2
]}

Li Q. ^{[3
]}

Liang K. ^{[3
]}

Li Y. ^{[3
]}

Kong N. ^{[4
]}

机构：

[1] Department of Computer Science, Purdue University, West Lafayette

[2] Department of Statistics and Mathematics, Purdue University, West Lafayette, IN

[3] Institute of Biophysics, Chinese Academy of Sciences, Beijing

[4] Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Dr, West Lafayette, 47906, IN

来源：

Journal of Healthcare Informatics Research | 2018年 / 2卷 / 3期

基金：

美国国家科学基金会;

关键词：

Binary classification; Blood sample; Feature ranking; Mass spectrometry; Proteome profiling;

D O I：

10.1007/s41666-018-0022-0

中图分类号：

学科分类号：

摘要：

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. © 2018, Springer International Publishing AG, part of Springer Nature.

引用

页码：305 / 318

页数：13

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