Data mining application to proteomic data from
mass spectrometry has gained much interest in recent years. Advances made in
proteomics and
mass spectrometry have resulted in considerable amount of data that cannot be easily visualized or interpreted. Mass spectral proteomic datasets are typically high dimensional but with small sample size. Consequently, advanced
artificial intelligence and
machine learning algorithms are increasingly being used for knowledge discovery from such datasets. Their overall goal is to extract useful information that leads to the identification of protein
biomarker candidates. Such
biomarkers could potentially have diagnostic value as tools for early detection, diagnosis, and prognosis of many diseases. The purpose of this review is to focus on the current trends in mining mass spectral proteomic data. Special emphasis is placed on the critical steps involved in the analysis of surface-enhanced laser desorption/ionization
mass spectrometry proteomic data. Examples are drawn from previously published studies and relevant
data mining terminology and techniques are exlained.
Relevant Topics in General Science
