Publications

@misc{Nordling20166thEast,

title = {Robust Network Inference–Testing of all possible influence hypotheses during model selection},

author = {Torbjörn E M Nordling},

url = {http://news-en.secr.ncku.edu.tw/files/14-1083-155190,r614-1.php?Lang=en},

year  = {2016},

date = {2016-06-01},

booktitle = {The 6th East Asia Mechanical and Aerospace Engineering Workshop},

publisher = {The 6th East Asia Mechanical and Aerospace Engineering Workshop in Tainan, Taiwan},

address = {National Cheng Kung University, Tainan, Taiwan (R.O.C.)},

abstract = {The main objective of network inference is to identify the structure, i.e. topology, of a network based on observed changes in state variables representing the nodes. For example, to infer genetic influences among genes based on measured expression changes in so called gene regulatory networks one need to test all possible influence hypotheses. Each influence hypothesis corresponds to a possible link in the network, which typically is represented by a model parameter. Network inference is thus model selection. In Robust Network Inference (RNI) the hypothesis—the link/parameter is not needed to explain the data—is tested for every possible link/parameter, based on the assumed uncertainty in the observed inputs and outputs of the system. RNI reveals the links that exist, i.e. must be present in order to explain the data within the chosen class of models. These links exist in reality, i.e. are true positives, assuming that the real system can be approximated using the chosen class of models and the real measurement error is smaller than the assumed uncertainty. Contrary to methods that infer the most likely model, RNI accounts for all possible models. For each possible link/parameter, Nordling's confidence score is calculated in RNI. Contrary to the marginal tests used so far, this score is a reliable significance measure for existence of the link, as well as a relative measure of the distance to the closest model lacking the link. By rejecting the hypothesis, e.g., the existence of a genetic influence or the importance of a parameter is proven using RNI.},

howpublished = {The 6th East Asia Mechanical and Aerospace Engineering Workshop},

keywords = {gene regulatory networks, Hypothesis testing, Nordling's confidence score, Reverse engineering, Robust network inference},

pubstate = {published},

tppubtype = {misc}

}