Unsupervised Algorithms for Microarray Sample Stratification

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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.

Original languageEnglish
Title of host publicationMicroarray Data Analysis
EditorsGiuseppe Agapito
PublisherHumana Press
Pages121-146
Number of pages26
ISBN (Electronic)978-1-0716-1839-4
ISBN (Print)978-1-0716-1841-7
DOIs
Publication statusPublished - 2022
Publication typeA3 Part of a book or another research book

Publication series

NameMethods in Molecular Biology
Volume2401
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Clustering
  • Dimensionality reduction
  • Group discovery
  • Microarray
  • Unsupervised learning

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Fingerprint

Dive into the research topics of 'Unsupervised Algorithms for Microarray Sample Stratification'. Together they form a unique fingerprint.

Cite this