@inbook{6f3978b5e23b4794950399a3b204bacc,
title = "Unsupervised Algorithms for Microarray Sample Stratification",
abstract = "The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.",
keywords = "Clustering, Dimensionality reduction, Group discovery, Microarray, Unsupervised learning",
author = "Michele Fratello and Luca Cattelani and Antonio Federico and Alisa Pavel and Giovanni Scala and Angela Serra 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\_9",
language = "English",
isbn = "978-1-0716-1841-7",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "121--146",
editor = "Giuseppe Agapito",
booktitle = "Microarray Data Analysis",
address = "United States",
}