Inferring Gene Networks from gene-expression data

The network representation of cellular regulatory systems allows for application of mathematical tools to gain insights into the fundamental organisation of living entities. Gene Networks (GNs) are abstract models of gene communication with nodes representing the gene activities (gene expression levels, mRNA concentrations), and directed edges representing causal influences. Many techniques for constructing GNs from gene expression data have been proposed and the most popular techniques are based on ordinary differential equations or Bayesian networks. In this seminar, the general problem of reverse-engineering gene networks will be outlined, the report on latest advances in this area and comments on further directions to pursue will be explained.

@InProceedings{De12,
  author       = {de la Fuente, A.},
  title        = {Inferring Gene Networks from gene-expression data},
  booktitle    = {Collana seminari interni 2012},
  number       = {20120502},
  month        = {may},
  year         = {2012},
  url          = {https://publications.crs4.it/pubdocs/2012/De12},
}