Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples
2
Citation
54
Reference
10
Related Paper
Citation Trend
Abstract:
The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis.Keywords:
Modularity
Interaction network
Gene regulatory network
Gene interaction
Interactome
Interaction network
Gene interaction
Gene regulatory network
Network Analysis
Protein Interaction Networks
Representation
Cite
Citations (133)
Modularity
Evolvability
Gene regulatory network
Cite
Citations (1)
Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organisms during biological activity such as cell cycle. A framework for gene network inference is to normalize gene expression data, discretize data, learn gene network and evaluate gene interactions. This framework was used to learn the gene network for two S. cerevisiae gene expression datasets (Spellman Cell cycle and Gasch Yeast Stress). Gene interaction inference was also done on data contained in 8 major clusters found by Spellman. The inferred networks were compared to gene interaction data curated by Biogrid. Results from the comparison shows that some of the inferred gene interactions agree with data contained in Biogrid and by referring to curated genetic interactions in Biogrid, we can understand the significance of computationally inferred gene interactions.
Gene regulatory network
Gene interaction
Pair-rule gene
Gene co-expression network
Cite
Citations (7)
Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at http://blaispathways.dfci.harvard.edu/metanet/.
Gene regulatory network
KEGG
Gene interaction
Cite
Citations (12)
ABSTRACT Single-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional “shape-space” describing distinct types of coexpression patterns. We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions.
Gene regulatory network
Stochastic modelling
Gene interaction
Cite
Citations (3)
Proteins interact with each other to regulate their functionality and localisation. The accumulated protein interaction evidences are represented by protein interaction network through a graph abstraction. Topological properties of protein interaction networks have been explored to characterise proteins and predict undiscovered interactions. Meanwhile, many researchers have tried to explain how protein interaction network is formed through evolutionary process. Moreover, the topological properties of protein interaction network are reported to have relationship with cancer-related genes. In this paper, we construct a weighted human protein interaction network based on triangles in protein interaction network and show that there is relationship between triangles in protein interaction network and phylogenetic age of proteins. We also show that triangles in protein interaction network are related to cancer-related genes.
Interaction network
Protein Interaction Networks
Abstraction
Gene interaction
Cite
Citations (0)
Abstract Background Gene regulatory networks control the global gene expression and the dynamics of protein output in living cells. In multicellular organisms, transcription factors and microRNAs are the major families of gene regulators. Recent studies have suggested that these two kinds of regulators share similar regulatory logics and participate in cooperative activities in the gene regulatory network; however, their combinational regulatory effects and preferences on the protein interaction network remain unclear. Methods In this study, we constructed a global human gene regulatory network comprising both transcriptional and post-transcriptional regulatory relationships, and integrated the protein interactome into this network. We then screened the integrated network for four types of regulatory motifs: single-regulation, co-regulation, crosstalk, and independent, and investigated their topological properties in the protein interaction network. Results Among the four types of network motifs, the crosstalk was found to have the most enriched protein-protein interactions in their downstream regulatory targets. The topological properties of these motifs also revealed that they target crucial proteins in the protein interaction network and may serve important roles of biological functions. Conclusions Altogether, these results reveal the combinatorial regulatory patterns of transcription factors and microRNAs on the protein interactome, and provide further evidence to suggest the connection between gene regulatory network and protein interaction network.
Interactome
Gene regulatory network
Crosstalk
Interaction network
Multicellular organism
Cite
Citations (53)
Modularity
Gene regulatory network
Interaction network
Cite
Citations (35)
Inferring gene regulatory networks by high-throughput data is a fundenmental problem in systems biology. The interactions between genes, proteins and other small molecules are typically described by gene regulatory networks, which are nonlinear and sparce. We linearize the nonlinear system of the segmentation polarity network of Drosophila melanogaster and infer the interaction between genes in the network by perturbation experimental data. The genes expression level are measured by microarray experiments. we calculate the parameters' changes forced by inputs of the experiment, and give a new method for experimental design in which the inputs facilitate precise estimation of the parameters. All the data in calculation is simulated in silico.
Gene regulatory network
Gene interaction
Experimental data
Identification
Cite
Citations (0)
Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships. Taken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.
Gene regulatory network
Boolean network
Gene interaction
Gene knockout
Interaction network
Cite
Citations (6)