Validating network - aedating software
Most published network inference methods attempt to validate their models through comparison with biological databases, calculating the proportion of interactions found both in the inferred networks and those databases .
An alternate route based on simulated interventional data was used in the NIPS2008 workshop on causality validating inferred networks by trying to predict the results of interventions , but this method is biased to those network inference models most closely resembling the simulation model.
Here we propose a new validation framework that enables a quantitative and unbiased assessment of the performance of an inferred network model.
This framework relies on generating independent, single-gene knock-down experiments targeting a collection of genes in a network or pathway of interest, and measuring gene expression data before and after the knock-downs.
With this data in hand, we apply the following iterative leave-one-out cross-validation approach to assess the performance of a given network inference method (This “dual use” of the data for model inference and validation allows the computation of a performance score that quantitatively assesses the inferred network's quality based on a comparison between the genes that are empirically determined to be affected based on the validation data set and those genes inferred to be affected based on the models.
We have increasingly come to recognize that cellular regulatory processes are more complex than we had once imagined and that it is generally not individual genes, but networks of interacting genes and gene products, which collectively interact to define phenotypes and the alterations that occur in the development of a disease .
Since this early work, there have been many other methods developed to model networks while addressing the intrinsic complexity of high-throughput genomic data (high feature-to-sample ratio and high level of noise) .
However, few methods have been widely used and often fail to produce useful network models, mainly because there are no gold standards on how to build and validate large gene networks  and .One challenge in developing network inference methods is validation of the resulting models.Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem.Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments.Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches.Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.