Learning a Markov Logic network for supervised gene regulatory network inference

Abstract

BackgroundGene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference 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 pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules.ResultsWe propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate “regulates”, starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions.ConclusionsThe numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge.

Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient

“…First, is the need to identify the two important parameters in GRN construction that are affected by noise: (1) the unaffected genes and (2) the wild-type strain values, which are more difficult to identify when a larger number of genes are involved. Second, though past research has been conducted in reconstructing GRN, only a few researchers applied their methods to real experimental GRN datasets, as an addition to synthetic data: [30], [41], [40], [43], [5], [44], [45], [46], [47], [21] and [6]. Third, most past research have focused on GRN prediction, with only minor attention given to determining the directionality of the genes.…”

“…We select a few researches from the year 2010 untill 2014 from reputable journals in bioinformatics such as PLOS ONE, PLOS Biology, PLOS Computational Biology, BMC Bioinformatics, IEEE/ ACM Transactions on Computational Biology and Bioinformatics and Oxford Journals: Bioinformatics. These recent researches apply methods such as Random Forests [5], multiple linear regression [36], particle swarm optimization and ant colony optimization [6], double t-test [37] , ANOVA [38], LASSO [39], Markov logic network [40], local expression pattern [26], univariate analysis [41], Kalman filter [42] , ordinary differential equation and distance correlation based [30]. From our observations, more attentions have been given to the detail of the method implementation itself rather than the choice of method.…”

Artificial Intelligence for Ecological and Evolutionary Synthesis

“…From there, they used Markov logic to weight the rules, adding enough flexibility to their system to beat the best approach of the time. Brouard et al (2013) used Markov logic to understand gene regulatory networks, noting how the resulting model provided clear insights, in contrast to more traditional machine learning techniques. Markov logic greatly simplifies the process of growing a base of knowledge: two research labs with different knowledge bases can simply put all their formulas in a single knowledge base.…”

Artificial Intelligence for Ecological and Evolutionary Synthesis

“…From there, they used Markov logic to weight the rules, adding enough flexibility to their system to beat the best approach of the time. Brouard et al [13] used Markov logic to understand gene regulatory networks, noting how the resulting model provided clear insights, in contrast to more traditional machine learning techniques. Markov logic greatly simplifies the process of growing a base of knowledge: two research labs with different knowledge bases can simply put all their formulas in a single knowledge base.…”