Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Default user image.

Carsten Peterson

Expert

Default user image.

Analyzing tumor gene expression profiles

Författare

  • Carsten Peterson
  • Markus Ringnér

Summary, in English

A brief introduction to high throughput technologies for measuring and analyzing gene expression is given. Various supervised and unsupervised data mining methods for analyzing the produced high-dimensional data are discussed. The main emphasis is on supervised machine learning methods for classification and prediction of tumor gene expression profiles. Furthermore, methods to rank the genes according to their importance for the classification are explored. The approaches are illustrated by exploratory studies using two examples of retrospective clinical data from routine tests; diagnostic prediction of small round blue cell tumors (SRBCT) of childhood and determining the estrogen receptor (ER) status of sporadic breast cancer. The classification performance is gauged using blind tests. These studies demonstrate the feasibility of machine learning-based molecular cancer classification.

Avdelning/ar

  • Computational Biology and Biological Physics - Undergoing reorganization

Publiceringsår

2003-05

Språk

Engelska

Sidor

59-74

Publikation/Tidskrift/Serie

Artificial Intelligence in Medicine

Volym

28

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Biophysics

Nyckelord

  • biomformatics
  • artificial neural networks
  • diagnostic prediction
  • target identification
  • drug
  • microarray
  • genes

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 1873-2860