@inbook{2cbc46683fa04856ab658dc1654651ab,
title = "Supervised Methods for Biomarker Detection from Microarray Experiments",
abstract = "Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection.",
keywords = "Biological validation, Biomarker, Classifier, Data unbalancing, Feature selection, Hyperparameter estimation, Microarray, Model selection, Multiomics, Validation metrics",
author = "Angela Serra and Luca Cattelani and Michele Fratello and Vittorio Fortino and Kinaret, {Pia Anneli Sofia} and Dario Greco",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2022",
doi = "10.1007/978-1-0716-1839-4_8",
language = "English",
isbn = "978-1-0716-1841-7",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "101--120",
editor = "Giuseppe Agapito",
booktitle = "Microarray Data Analysis",
address = "United States",
}