Journal Article

A boosting approach to structure learning of graphs with and without prior knowledge

Shahzia Anjum, Arnaud Doucet and Chris C. Holmes

in Bioinformatics

Volume 25, issue 22, pages 2929-2936
Published in print November 2009 | ISSN: 1367-4803
Published online August 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp485
A boosting approach to structure learning of graphs with and without prior knowledge

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Motivation: Identifying the network structure through which genes and their products interact can help to elucidate normal cell physiology as well as the genetic architecture of pathological phenotypes. Recently, a number of gene network inference tools have appeared based on Gaussian graphical model representations. Following this, we introduce a novel Boosting approach to learn the structure of a high-dimensional Gaussian graphical model motivated by the applications in genomics. A particular emphasis is paid to the inclusion of partial prior knowledge on the structure of the graph. With the increasing availability of pathway information and large-scale gene expression datasets, we believe that conditioning on prior knowledge will be an important aspect in raising the statistical power of structural learning algorithms to infer true conditional dependencies.

Results: Our Boosting approach, termed BoostiGraph, is conceptually and algorithmically simple. It complements recent work on the network inference problem based on Lasso-type approaches. BoostiGraph is computationally cheap and is applicable to very high-dimensional graphs. For example, on graphs of order 5000 nodes, it is able to map out paths for the conditional independence structure in few minutes. Using computer simulations, we investigate the ability of our method with and without prior information to infer Gaussian graphical models from artificial as well as actual microarray datasets. The experimental results demonstrate that, using our method, it is possible to recover the true network topology with relatively high accuracy.

Availability: This method and all other associated files are freely available from http://www.stats.ox.ac.uk/∼anjum/.

Contact: s.anjum@har.mrc.ac.uk; cholmes@stats.ox.ac.uk

Supplementary information: Supplementary data are available at Bioinfomatics online.

Journal Article.  5905 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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