Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

Abstract

This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.

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

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An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks

“…A novel method of modeling gene networks is via the usage of qualitative probabilistic networks (QPNs), which represent the qualitative analog of the DBNs [18]. The structural and independence properties of QPNs are the same as those of Bayesian networks.…”

Modeling with linguistic entities and linguistic descriptors: a perspective of granular computing

“…Qualitative modeling In qualitative modeling (Bolt et al 2005;Ibrahim et al 2011;Liu et al 2008;Shults and Kuipers 1997;Forbus 1996), we are concerned with a description of real-world phenomena carried out at the level of symbolic entities. The focal point is about expressing and reasoning about some conceptual landmarks articulated in terms of symbols.…”