MOTIVATION: Knowledge Graph Completion has been increasingly adopted as a useful method for helping address several tasks in biomedical research, such as drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models have been proposed over the years. However, little is known about the properties that render a dataset, and associated modelling choices, useful for a given task. Moreover, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. RESULTS: In this work, we conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world tasks. By releasing all model predictions and a new suite of analysis tools we invite the community to build upon our work and continue improving the understanding of these crucial applications. AVAILABILITY AND IMPLEMENTATION: The code used to perform experiments and analyze results in this article as well as all experimental data is available at https://github.com/graphcore-research/kg-topology-toolbox/tree/main/the_role_of_graph_topology_paper and archived on Zenodo, at https://doi.org/10.5281/zenodo.12097376.

MOTIVATION: Gene expression measurements are the most common data source for reverse engineering gene interaction networks. When dealing with destructive sampling in time course experiments, it is common to average any available measurements for each time point and to treat this as the actual time series data for fitting the network, neglecting the variability contained in the repeated measurements. Proceeding in such a way can affect the retrieved network topology. RESULTS: We propose a fully Bayesian method for reverse engineering a gene interaction network, based on time course data with repeated measurements. The observations are treated as surrogate measurements of the underlying gene expression. As these measurements often contain outliers, we use a non-Gaussian specification for dealing with measurement error. The network interactions are assumed linear and an autoregressive model is specified, augmented with indicator variables that allow inference on the topology of the network. We analyse two in silico and one in vivo experiments, the latter dealing with the circadian clock in Arabidopsis thaliana. A systematic attenuation of the estimated regulation strengths and a concomitant overestimation of their precision is demonstrated when measurement error is disregarded. Thus, a clear improvement in the inferred topology for the synthetic datasets is demonstrated when this is included. Also, the influence of outliers in the retrieved network is demonstrated when using the in vivo data. AVAILABILITY: Matlab code and data used in the article are available from http://go.warwick.ac.uk/majuarez/home/materials.