Skip Navigation

Bioinformatics 2007 23(13):i519-i528; doi:10.1093/bioinformatics/btm181
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Sysi-Aho, M.
Right arrow Articles by Oresic, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Sysi-Aho, M.
Right arrow Articles by Oresic, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles

Marko Sysi-Aho 1,*, Aki Vehtari 2, Vidya R. Velagapudi 1, Jukka Westerbacka 3, Laxman Yetukuri 1, Robert Bergholm 3, Marja-Riitta Taskinen 3, Hannele Yki-Järvinen 4 and Matej Oresic 1

1VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT2Helsinki University of Technology, Espoo, FI-02015 TKK3Helsinki University Hospital, Biomedicum, P.O. Box 700, 000290 Helsinki4Department of Medicine, University of Helsinki, Biomedicum, 00290 Helsinki

*To whom correspondence should be addressed.


  Abstract

Motivation: Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today.

Results: We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.

Contact: marko.sysi-aho{at}vtt.fi


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.