Availability of highthroughput gene expression data has lead to numerous attempts to infer network models of gene regulation based on expression changes. The low number of observations compared to the number of genes, the low signaltonoise ratios, and the system being interampatte make the inference problem illposed and challenging. To solve the problem a majority of all published approaches resort to regularization, e.
g. the LASSO penalty is used to find a sparse model. Regularization is known to introduce a bias, but its effect on inferred gene regulatory networks has hardly been investigated. In machine learning and compressed sensing, where regularization has been widely applied and studied, the objective is to reproduce a signal and the actual variable selection is of minor importance as long as the signal is reproduced well.
In network inference, on the other hand, the variable selection is crucial since we want to identify the true topology of the network and a minimal number of links is not an aim per se. We first study the inference problem in a deterministic setting in order to gain insight and derive conditions on when the regularization causes false negative and positive links.
By viewing the problem as a parameter identifiability problem, we establish three cases in which a subset of the parameters can be uniquely determined. Finally we devise conditions for invalidation of the inferred links using existing or additional data; resulting in an iterative procedure of inference and experiment design that significantly increases the confidence in the inferred network model.
Verk av författare med samma namn, Torbjörn E. M. Nordling Obs! Kan vara andra personer 


2005  Wavelength selection by genetic algorithms in near.. 
2006  Experiment Design for Systematic Excitation of Gen.. 
2007  Deduction of intracellular subsystems from a topo.. 
2008  IllConditioning 
2008  Inference of interampatte gene regulatory networks 
2011  On Sparsity As a Criterion in Reconstructing Bioch.. 
2013  Robust inference of gene regulatory networks 