Learning a Markov logic network for supervised gene regulation inference: application to the ID2 regulatory network in human keratinocytes
published: Oct. 23, 2012, recorded: September 2012, views: 3143
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
Motivation: Gene regulatory network inference remains a challenging problem in systems biology
despite numerous approaches. When substantial knowledge on a gene regulatory network is
already available, supervised network inference also is appropriate. Such a method builds a binary
classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once
learnt, the classifier can be used to predict new regulations. In this work, we explore the
framework of Markov Logic Network (MLN) recently introduced by Richardson & Domingos (2004,
2006). A MLN is a random Markov network that codes for a set of weighted formula. It therefore
combines features of probabilistic graphical models with the expressivity of 1st order logic rules.
Results: Starting from a known gene regulatory network involved in the switch proliferation
differentiation of keratinocytes cells, a set of experimental transcriptomic data, and description of
genes in terms of GO terms encoded into first order logic, we learn a Markov Logic network, e.g. a
set of weighted rules that conclude on the predicate ”regulates”. As a side contribution, we define
a list of basic tests for performance assessment, valid for any binary classifier. A first test consists
of measuring the average performance on balanced edge prediction problem; a 2nd one deals with
the ability of the classifier, once enhanced by asymmetric bagging, to update a given network;
finally a 3rd test measures the ability of the method to predict new interactions with new genes.
Conclusion: The numerical studies show that MLNs achieve very good prediction while opening the
door to some interpretability of the decisions. Additionally to the ability to suggest new regulation,
such an approach allows to cross-validate experimental data with existing knowledge.
Availability: The code will be available on demand.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: