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- Title
Genetic network inference: from co-expression clustering to reverse engineering.
- Authors
D'haeseleer, P; Liang, S; Somogyi, R
- Abstract
Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.
- Publication
Bioinformatics (Oxford, England), 2000, Vol 16, Issue 8, p707
- ISSN
1367-4803
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/16.8.707