Learning a Markov logic network for supervised gene regulation inference: application to the ID2 regulatory network in human keratinocytes

author: Florence d'Alche-Buc, Université Evry Val d'Essonne
published: Oct. 23, 2012,   recorded: September 2012,   views: 140


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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.

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