Probabilistic network sparsification with ego betweenness

Sparsification is the process of decreasing the number of edges in a network while one or more topological properties are preserved. For probabilistic networks, sparsification has only been studied to preserve the expected degree of the nodes. In this work we introduce a sparsification method to preserve ego betweenness. Moreover, we study the effect of backboning and density on the resulting sparsified networks. Our experimental results show that the sparsification of high density networks can be used to efficiently and accurately estimate measures from the original network, with the choice of backboning algorithm only partially affecting the result.

Probabilistic graphlets capture biological function in probabilistic molecular networks

Motivation Molecular interactions have been successfully modeled and analyzed as networks, where nodes represent molecules and edges represent the interactions between them. These networks revealed that molecules with similar local network structure also have similar biological functions. The most sensitive measures of network structure are based on graphlets. However, graphlet-based methods thus far are only applicable to unweighted networks, whereas real-world molecular networks may have weighted edges that can represent the probability of an interaction occurring in the cell. This information is commonly discarded when applying thresholds to generate unweighted networks, which may lead to information loss. Results We introduce probabilistic graphlets as a tool for analyzing the local wiring patterns of probabilistic networks. To assess their performance compared to unweighted graphlets, we generate synthetic networks based on different well-known random network models and edge probability distributions and demonstrate that probabilistic graphlets outperform their unweighted counterparts in distinguishing network structures. Then we model different real-world molecular interaction networks as weighted graphs with probabilities as weights on edges and we analyze them with our new weighted graphlets-based methods. We show that due to their probabilistic nature, probabilistic graphlet-based methods more robustly capture biological information in these data, while simultaneously showing a higher sensitivity to identify condition-specific functions compared to their unweighted graphlet-based method counterparts. Availabilityand implementation Our implementation of probabilistic graphlets is available at https://github.com/Serdobe/Probabilistic_Graphlets. Supplementary information Supplementary data are available at Bioinformatics online.

Synthetic data generation with probabilistic Bayesian Networks

Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct probabilistic networks from the large heterogeneous biological datasets that reflect the underlying networks of biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The latter is arguably the most comprehensive approach; however, existing implementations are typically limited by their reliance on the SEM (structural equation modeling) framework, which includes many explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario. In this study, we develop an alternative, purely probabilistic, simulation framework that more appropriately fits with real biological data and biological network models. In conjunction, we also expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.

Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction

Background: Type 2 Diabetes is a chronic disease with an onset that is commonly associated with multiple life-threatening comorbidities (complications). Early prediction of diabetic complications while discovering the behaviour of associated aggressive risk factors can reduce the patients’ suffering time. Therefore, models of the time series diabetic data (which are often imbalanced, incomplete and involve complex interactions) are needed to better manage diabetic complications.Aims: The aim of this work is to both deals with imbalanced clinical data using a bootstrapping approach, whilst determining the precise position of latent variables within probabilistic networks generated from the observations. The main motivation behind this paper is to stratify patient groups by means of latent variables to discover how complications in diabetes interact.Methods: We propose a time series bootstrapping method for building Dynamic Bayesian Networks that includes hidden/latent variables, applied to a case for predicting T2DM complications. A combination of the IC* algorithm on time series bootstrapped data is utilised to identify the latent variables within a Bayesian model. Then, an exploration of inference methods assessed the influences of these latent variables.Results: Our promising findings show how this targeted use of latent variables improves prediction accuracy, specificity, and sensitivity over standard approaches as well as aiding the understanding of relationships between these latent variables and disease complications/risk factors. The contribution of this paper compared to the previous papers in which time series bootstrapping is used for re-balancing the data and providing confidence in the prediction results.Conclusion: Our results showed that our re-balancing approach by the use of Time Series bootstrapping method for an unequal number of time series visits demonstrated an improvement in the prediction performance. Additionally, the most highlighted contribution of this paper gained insight by interpreting the latent states (looking at the associated distributions of complications), which led to a better understanding of risk factors and patient-specific interventions: here the fact that the latent variable demonstrated that a patient falls into a sub-group that is hypertensive but not suffering from retinopathy.