Classification methods, i.e., the chemometric strategies for predicting a qualitative response, find many applications in the omic sciences, where often data are collected in order to categorize individuals (e.g. according to whether they were treated or administered a placebo or, for instance, depending on if they were healthy or ill). After a brief discussion of the differences between discriminant and modelling approaches, some of the techniques most commonly used in the omic fields are illustrated in greater detail. A part of the chapter is then devoted to illustrating the strategies for identifying the most relevant features in model building through variable selection approaches, and their role in putative biomarker identification. Lastly, the importance of validation is also addressed, and a brief guideline of the available strategies is presented.

Chemometric methods for classification and feature selection / Cocchi, M.; Biancolillo, A.; Marini, F.. - (2018), pp. 265-299. - COMPREHENSIVE ANALYTICAL CHEMISTRY. [10.1016/bs.coac.2018.08.006].

Chemometric methods for classification and feature selection

Biancolillo A.
Secondo
;
Marini F.
Ultimo
2018

Abstract

Classification methods, i.e., the chemometric strategies for predicting a qualitative response, find many applications in the omic sciences, where often data are collected in order to categorize individuals (e.g. according to whether they were treated or administered a placebo or, for instance, depending on if they were healthy or ill). After a brief discussion of the differences between discriminant and modelling approaches, some of the techniques most commonly used in the omic fields are illustrated in greater detail. A part of the chapter is then devoted to illustrating the strategies for identifying the most relevant features in model building through variable selection approaches, and their role in putative biomarker identification. Lastly, the importance of validation is also addressed, and a brief guideline of the available strategies is presented.
2018
Data analysis for omic sciences: methods and applications
9780444640444
class modelling; classification; discrimination; linear discriminant analysis; partial least squares-discriminant analysis (PLS-DA); validation; variable selection
02 Pubblicazione su volume::02a Capitolo o Articolo
Chemometric methods for classification and feature selection / Cocchi, M.; Biancolillo, A.; Marini, F.. - (2018), pp. 265-299. - COMPREHENSIVE ANALYTICAL CHEMISTRY. [10.1016/bs.coac.2018.08.006].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1353146
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