Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism

<p>Abstract</p> <p>Background</p> <p>Dynamic Bayesian Networks (DBNs) are widely used in regulatory network structure inference with gene expression data. Current methods assumed that the underlying stochastic processes that generate the gene expression data are stationary. The assumption is not realistic in certain applications where the intrinsic regulatory networks are subject to changes for adapting to internal or external stimuli.</p> <p>Results</p> <p>In this paper we investigate a novel non-stationary DBNs method with a potential regulator detection technique and a flexible lag choosing mechanism. We apply the approach for the gene regulatory network inference on three non-stationary time series data. For the Macrophages and Arabidopsis data sets with the reference networks, our method shows better network structure prediction accuracy. For the Drosophila data set, our approach converges faster and shows a better prediction accuracy on transition times. In addition, our reconstructed regulatory networks on the Drosophila data not only share a lot of similarities with the predictions of the work of other researchers but also provide many new structural information for further investigation.</p> <p>Conclusions</p> <p>Compared with recent proposed non-stationary DBNs methods, our approach has better structure prediction accuracy By detecting potential regulators, our method reduces the size of the search space, hence may speed up the convergence of MCMC sampling.</p

Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism

Background: Dynamic Bayesian Networks (DBNs) are widely used in regulatory network structure inference with gene expression data. Current methods assumed that the underlying stochastic processes that generate the gene expression data are stationary. The assumption is not realistic in certain applications where the intrinsic regulatory networks are subject to changes for adapting to internal or external stimuli. Results: In this paper we investigate a novel non-stationary DBNs method with a potential regulator detection technique and a flexible lag choosing mechanism. We apply the approach for the gene regulatory network inference on three non-stationary time series data. For the Macrophages and Arabidopsis data sets with the reference networks, our method shows better network structure prediction accuracy. For the Drosophila data set, our approach converges faster and shows a better prediction accuracy on transition times. In addition, our reconstructed regulatory networks on the Drosophila data not only share a lot of similarities with the predictions of the work of other researchers but also provide many new structural information for further investigation. Conclusions: Compared with recent proposed non-stationary DBNs methods, our approach has better structure prediction accuracy By detecting potential regulators, our method reduces the size of the search space, hence may speed up the convergence of MCMC sampling

Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism

Background Dynamic Bayesian Networks (DBNs) are widely used in regulatory network structure inference with gene expression data. Current methods assumed that the underlying stochastic processes that generate the gene expression data are stationary. The assumption is not realistic in certain applications where the intrinsic regulatory networks are subject to changes for adapting to internal or external stimuli. Results In this paper we investigate a novel non-stationary DBNs method with a potential regulator detection technique and a flexible lag choosing mechanism. We apply the approach for the gene regulatory network inference on three non-stationary time series data. For the Macrophages and Arabidopsis data sets with the reference networks, our method shows better network structure prediction accuracy. For the Drosophila data set, our approach converges faster and shows a better prediction accuracy on transition times. In addition, our reconstructed regulatory networks on the Drosophila data not only share a lot of similarities with the predictions of the work of other researchers but also provide many new structural information for further investigation. Conclusions Compared with recent proposed non-stationary DBNs methods, our approach has better structure prediction accuracy By detecting potential regulators, our method reduces the size of the search space, hence may speed up the convergence of MCMC sampling

Online Spectral Clustering on Network Streams

Graph is an extremely useful representation of a wide variety of practical systems in data analysis. Recently, with the fast accumulation of stream data from various type of networks, significant research interests have arisen on spectral clustering for network streams (or evolving networks). Compared with the general spectral clustering problem, the data analysis of this new type of problems may have additional requirements, such as short processing time, scalability in distributed computing environments, and temporal variation tracking. However, to design a spectral clustering method to satisfy these requirements certainly presents non-trivial efforts. There are three major challenges for the new algorithm design. The first challenge is online clustering computation. Most of the existing spectral methods on evolving networks are off-line methods, using standard eigensystem solvers such as the Lanczos method. It needs to recompute solutions from scratch at each time point. The second challenge is the parallelization of algorithms. To parallelize such algorithms is non-trivial since standard eigen solvers are iterative algorithms and the number of iterations can not be predetermined. The third challenge is the very limited existing work. In addition, there exists multiple limitations in the existing method, such as computational inefficiency on large similarity changes, the lack of sound theoretical basis, and the lack of effective way to handle accumulated approximate errors and large data variations over time. In this thesis, we proposed a new online spectral graph clustering approach with a family of three novel spectrum approximation algorithms. Our algorithms incrementally update the eigenpairs in an online manner to improve the computational performance. Our approaches outperformed the existing method in computational efficiency and scalability while retaining competitive or even better clustering accuracy. We derived our spectrum approximation techniques GEPT and EEPT through formal theoretical analysis. The well established matrix perturbation theory forms a solid theoretic foundation for our online clustering method. We facilitated our clustering method with a new metric to track accumulated approximation errors and measure the short-term temporal variation. The metric not only provides a balance between computational efficiency and clustering accuracy, but also offers a useful tool to adapt the online algorithm to the condition of unexpected drastic noise. In addition, we discussed our preliminary work on approximate graph mining with evolutionary process, non-stationary Bayesian Network structure learning from non-stationary time series data, and Bayesian Network structure learning with text priors imposed by non-parametric hierarchical topic modeling

Feeling the Heat: Investigating the dual assault of Zymoseptoria tritici and Heat Stress on Wheat (Triticum aestivum)

As a result of climate change, field conditions are increasingly challenging for crops. Research has shown how elevated temperatures affect crop performance, yet the impact of temperature on host-pathogen relationships remains unknown. Understanding the effects of combined abiotic and biotic stresses on crop plants and the plant-microbial interaction is crucial in developing strategies to improve crop stress tolerance and manage diseases effectively. Lipids sense, signal, and mitigate temperature elevation effects, and lipid remodelling plays a key role in the plant and fungal response to heat stress. Our study uses a systems approach to examine the Z. tritici wheat model system, combining transcriptomics, lipidomics, and phenotyping to decipher the impact of high-temperature stress on the plant-pathogen interaction. Microscopy in vivo and RNA-Seq analyses confirmed that Z. tritici responds to high-temperature treatments with morphological and transcriptomic changes. Temperature-related configuration of the transcriptome was associated with the accessory chromosomes and expression of ‘accessory’ pan-genome-derived genes. Metabolism-related gene expression predominated, indicated by GO enrichment and analysis of KOG classes, and large-scale lipid remodelling was likely given the proportion of lipid transport and metabolism-related expression changes in response to temperature. Changes in lipid content and composition were then validated by LC-MS analysis. Heat-responsive fungal genes and pathways, including scramblase family genes, are being tested by reverse genetics to ascertain their importance for fungal adaption to elevated temperatures. Elevated temperature schemes were applied to wheat to study the impact of combined stress on the plant-pathogen interaction, based on long-term climate data from Rothamsted Research, using transcriptomic, lipidomic and phenotypic analyses. Comparing non-infected and infected wheat plants under typical and elevated temperatures. Our initial analysis of the transcriptomic data indicates a delay in the development of Z. tritici, followed by its adaptation to the warmer environment. Once the infection was established, the fungus exhibited resilience to the impact of higher external temperatures. Our results indicate that temperature elevations associated with climate change directly impact plant-pathogen interactions. Furthermore, the study demonstrates a need for further detailed understanding to sustain crop resilience