In my talk, I will give an overview about modern machine learning techniques for analysing the huge number of features in metabolite data. I will introduce Independent Component Analysis (ICA) as a useful alternative to classical Principal Component Analysis. I will show in which cases a Support Vector Machine (SVM) can be appropriate for biomarker detection. And I will talk about time course analysis and how to analysis and visualize the nonlinear data structure. This will be completed by showing some examples in Matlab.

Scholz, M.U. (2012). Visualization of metabolomics data. In: 2nd Workshop on Holistic Analytical Methods for Systems Biology Studies, Athens, Greece, November 11-12, 2012: 16. handle: http://hdl.handle.net/10449/22017

Visualization of metabolomics data

Scholz, Matthias Uwe
2012-01-01

Abstract

In my talk, I will give an overview about modern machine learning techniques for analysing the huge number of features in metabolite data. I will introduce Independent Component Analysis (ICA) as a useful alternative to classical Principal Component Analysis. I will show in which cases a Support Vector Machine (SVM) can be appropriate for biomarker detection. And I will talk about time course analysis and how to analysis and visualize the nonlinear data structure. This will be completed by showing some examples in Matlab.
2012
Scholz, M.U. (2012). Visualization of metabolomics data. In: 2nd Workshop on Holistic Analytical Methods for Systems Biology Studies, Athens, Greece, November 11-12, 2012: 16. handle: http://hdl.handle.net/10449/22017
File in questo prodotto:
File Dimensione Formato  
2012 scholz.pdf

accesso aperto

Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 54.26 kB
Formato Adobe PDF
54.26 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/22017
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact