title: Feature selection and machine learning with mass spectrometry data.
abstract: Mass spectrometry has been used in biochemical research for a long time. However, its potential for discovering proteomic biomarkers using protein mass spectra has aroused tremendous interest in the last few years. In spite of its potential for biomarker discovery, it is recognized that the identification of meaningful proteomic features from mass spectra needs careful evaluation. Hence, extracting meaningful features and discriminating the samples based on these features are still open areas of research. Several research groups are actively involved in making the process as perfect as possible. In this chapter, we provide a review of major contributions toward feature selection and classification of proteomic mass spectra involving MALDI-TOF and SELDI-TOF technology.
authors: Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA.
authors: Susmita
authors: S
authors: Datta
authors: Vasyl
authors: V
authors: Pihur
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D011786:Quality Control; D012984:Software