Potential Sub-networks Research Articles

A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification

AbstractMotivationDifferential network (D-Net) analysis has attracted great attention in systems biology for its ability to identify genetic variations in response to different conditions. Current approaches either estimate the condition-specific networks separately followed by post-procedures to determine the differential edges or estimate the D-Net directly. Both types of analysis overlook the probabilistic inference and can only provide deterministic inference of the edges.ResultsHere, we propose a Bayesian solution and translate the probabilistic estimation in the regression model to an inferential D-Net analysis for genetic association and classification studies. The proposed PRobabilistic Interaction for Differential Edges (PRIDE) focuses on inferring the D-Net with uncertainty so that the existence of the differential edges can be evaluated with probability and even prioritized if comparison among these edges is of interest. The performance of the proposed model is compared with state-of-the-art methods in simulations and is demonstrated in glioblastoma and breast cancer studies. The proposed PRIDE performs comparably to or outperforms most existing tools under deterministic evaluation criteria. Additionally, it offers the unique advantages, including prioritizing the differential edges with probabilities, highlighting the relative importance of hub nodes, and identifying potential sub-networks in a D-Net.Availability and implementationAll the data analyzed in this research can be downloaded at https://xenabrowser.net/datapages/. The R code for implementing PRIDE is available at https://github.com/YJGene0806/PRIDE_Code.

Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis.

Finding the biomarkers associated with autism spectrum disorder (ASD) is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatments. In essence, we are faced with two challenges (i) how to learn a node representation and a clean graph structure from original graph data with high dimensionality and (ii) how to jointly model the procedure of node representation learning, structure learning and graph classification. We propose FSL-BrainNet, an interpretable graph convolution network (GCN) model for jointly Learning of node Features and clean Structures in brain networks for automatic brain network classification and interpretation. We formulate an end-to-end trainable and interpretable framework for graph classification and biomarkers (salient brain regions and potential subnetworks) identification. The experimental results on the ABIDE dataset show that our proposed methods not only achieve improved prediction performance compared with the state-of-the-art methods, but also find a compact set of highly suggestive biomarkers including relevant brain regions and subnetworks to ASD. Through node feature learning and structure learning, our model can simultaneously select important brain regions and identify subnetworks.

Transcriptome analysis for identifying possible gene regulations during maize root emergence and formation at the initial growth stage.

The root plays an important role during plant development and growth, i.e., the plant body maintenance, nutrient storage, absorption of water, oxygen and nutrient from the soil, and storage of water and carbohydrates, etc. The objective of this study was attempted to determine root-specific genes at the initial developmental stages of maize by using network-based transcriptome analysis. The raw data obtained using RNA-seq were filtered for quality control of the reads with the FASTQC tool, and the filtered reads were pre-proceed using the TRIMMOMATIC tool. The enriched BINs of the DEGs were detected using PageMan analysis with the ORA_FISHER statistical test, and genes were assigned to metabolic pathways by using the MapMan tool, which was also used for detecting transcription factors (TFs). For reconstruction of the co-expression network, we used the algorithm for the reconstruction of accurate cellular networks (ARACNE) in the R package, and then the reconstructed co-expression network was visualized using the Cytoscape tool. RNA-seq. was performed using maize shoots and roots at different developmental stages of root emergence (6-10 days after planting, VE) and 1 week after plant emergence (V2). A total of 1286 differentially expressed genes (DEGs) were detected in both tissues. Many DEGs involved in metabolic pathways exhibited altered mRNA levels between VE and V2. In addition, we observed gene expression changes for 113 transcription factors and found five enriched cis-regulatory elements in the 1-kb upstream regions of both DEGs. The network-based transcriptome analysis showed two modules as co-expressed gene clusters differentially expressed between the shoots and roots during plant development. The DEGs of one module exhibited gene expressional coherence in the maize root tips, suggesting that their functional relationships are associated with the initial developmental stage of the maize root. Finally, we confirmed reliable mRNA levels of the hub genes in the potential sub-network related to initial root development at the different developmental stages of VE, V2, and 2 weeks after plant emergence.

Definition and characterization of an extended multiple-demand network

Neuroimaging evidence suggests that executive functions (EF) depend on brain regions that are not closely tied to specific cognitive demands but rather to a wide range of behaviors. A multiple-demand (MD) system has been proposed, consisting of regions showing conjoint activation across multiple demands. Additionally, a number of studies defining networks specific to certain cognitive tasks suggest that the MD system may be composed of a number of sub-networks each subserving specific roles within the system. We here provide a robust definition of an extended MDN (eMDN) based on task-dependent and task-independent functional connectivity analyses seeded from regions previously shown to be convergently recruited across neuroimaging studies probing working memory, attention and inhibition, i.e., the proposed key components of EF. Additionally, we investigated potential sub-networks within the eMDN based on their connectional and functional similarities. We propose an eMDN network consisting of a core whose integrity should be crucial to performance of most operations that are considered higher cognitive or EF. This then recruits additional areas depending on specific demands.

Tracing and Characterizing the Development of Transplanted Female Germline Stem Cells In Vivo

It has long been believed that most female mammalian species lose the ability to generate oocytes in postnatal ovaries. Recent evidence has demonstrated the isolation and culture of female germline stem cells (FGSCs) from adult mice and humans. However, the process and mechanisms of FGSC differentiation invivo following transplantation have not yet been studied. Here, we isolated and characterized FGSCs from a single EGFP-transgenic mouse, and traced the development and behavior of transplanted FGSCs (F-TFs) invivo. Comparisons of folliculogenesis between recipients with FGSC transplantation and wild-type (WT) mice were performed by single follicle RNA-sequencing (RNA-seq). Results showed that FGSCs exhibited a homing ability and began to differentiate into early-stage oocytes only when they reached the edge of the ovarian cortex. The F-TFs restored function of premature ovarian failure (gdf9iCre; PtenloxP/loxP genotype) and generated offspring. Furthermore, results demonstrated that the developmental mechanisms of follicles derived from F-TFs were similar to that of WT follicles. Weighted gene co-expression network analysis identified two potential sub-networks and core genes that played a critical role in follicular development. These findings provide a theoretical basis and lay a technology platform for specific or personalized medical treatment of ovarian failure or other ovarian diseases.

Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network.

BackgroundDiscovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, “modular markers,” that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms.ResultsIn this work, we propose a novel algorithm for finding robust and effective subnetwork markers that can accurately predict cancer prognosis. To simultaneously discover multiple synergistic subnetwork markers in a human PPI network, we build on our previous work that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme. Using affinity propagation, we identify potential subnetwork markers that consist of discriminative genes that display coherent expression patterns and whose protein products are closely located on the PPI network. Furthermore, we incorporate the topological information from the PPI network to evaluate the potential of a given set of proteins to be involved in a functional module. Primarily, we adopt widely made assumptions that densely connected subnetworks may likely be potential functional modules and that proteins that are not directly connected but interact with similar sets of other proteins may share similar functionalities.ConclusionsIncorporating topological attributes based on these assumptions can enhance the prediction of potential subnetwork markers. We evaluate the performance of the proposed subnetwork marker identification method by performing classification experiments using multiple independent breast cancer gene expression datasets and PPI networks. We show that our method leads to the discovery of robust subnetwork markers that can improve cancer classification.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1224-1) contains supplementary material, which is available to authorized users.

Convergence and divergence of genetic and modular networks between diabetes and breast cancer.

Diabetes mellitus (DM) and breast cancer (BC) can simultaneously occur in the same patient populations, but the molecular relationship between them remains unknown. In this study, we constructed genetic networks and used modularized analysis approaches to investigate the multi-dimensional characteristics of two diseases and one disease subtype. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to validate potential subnetworks and to divide the modules, respectively. A total of 793 DM-related genes, 386 type 2 diabetes (T2DM) genes and 873 BC-related genes were identified from the Online Mendelian Inheritance in Man database. For DM and BC, a total of 99 overlapping genes, 9 modules, 29 biological processes and 7 pathways were identified. Meanwhile, for T2DM and BC, 56 overlapping genes, 5 modules, 20 biological processes and 12 pathways were identified. Based on the Gene Ontology functional enrichment analysis of the top 10 non-overlapping modules of the two diseases, 10 biological functions and 5 pathways overlapped between them. The glycosphingolipid and lysosome pathways verified molecular mechanisms of cell death related to both DM and BC. We also identified new biological functions of dopamine receptors and four signalling pathways (Parkinson's disease, Alzheimer's disease, Huntington's disease and long-term depression) related to both diseases; these warrant further investigation. Our results illustrate the landscape of the novel molecular substructures between DM and BC, which may support a new model for complex disease classification and rational therapies for multiple diseases.

Inference of gene regulatory subnetworks from time course gene expression data

BackgroundIdentifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN reconstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to others in the sub-networks.ResultsWe propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and can promisingly be applied to large-scale GRNs. Furthermore, we present the efficient Block PCA method for searching communities in GRNs.ConclusionsThe NCI method is effective in identifying multiple subnetworks in a large-scale GRN. With the splitting algorithm, the Block PCA method shows a promosing attempt for exploring communities in a large-scale GRN.