Product graph representations

1992 ◽  
Vol 16 (5) ◽  
pp. 467-488 ◽  
Author(s):  
Tomás Feder
Keyword(s):  
Graph Representations ◽  
Product Graph

scholarly journals Applying graph database technology for analyzing perturbed co-expression networks in cancer

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Claire M Simpson ◽  
Florian Gnad
Keyword(s):  
Relational Databases ◽  
Molecular Mechanisms ◽  
Biological Data ◽  
Database Management System ◽  
Graph Database ◽  
Graph Databases ◽  
Graph Representations ◽  
Rnaseq Data ◽  
Database Technology ◽  
Speed Accuracy

Abstract Graph representations provide an elegant solution to capture and analyze complex molecular mechanisms in the cell. Co-expression networks are undirected graph representations of transcriptional co-behavior indicating (co-)regulations, functional modules or even physical interactions between the corresponding gene products. The growing avalanche of available RNA sequencing (RNAseq) data fuels the construction of such networks, which are usually stored in relational databases like most other biological data. Inferring linkage by recursive multiple-join statements, however, is computationally expensive and complex to design in relational databases. In contrast, graph databases store and represent complex interconnected data as nodes, edges and properties, making it fast and intuitive to query and analyze relationships. While graph-based database technologies are on their way from a fringe domain to going mainstream, there are only a few studies reporting their application to biological data. We used the graph database management system Neo4j to store and analyze co-expression networks derived from RNAseq data from The Cancer Genome Atlas. Comparing co-expression in tumors versus healthy tissues in six cancer types revealed significant perturbation tracing back to erroneous or rewired gene regulation. Applying centrality, community detection and pathfinding graph algorithms uncovered the destruction or creation of central nodes, modules and relationships in co-expression networks of tumors. Given the speed, accuracy and straightforwardness of managing these densely connected networks, we conclude that graph databases are ready for entering the arena of biological data.

A New Technique Based on Loops to Investigate Displacement Isomorphism in Planetary Gear Trains

2002 ◽  
Vol 124 (4) ◽  
pp. 662-675 ◽  
Author(s):  
V. V. N. R. Prasad Raju Pathapati ◽  
A. C. Rao
Keyword(s):  
Graph Isomorphism ◽  
Planetary Gear ◽  
Degree Of Freedom ◽  
Gear Train ◽  
Graph Representation ◽  
Kinematic Structure ◽  
Graph Representations ◽  
Planetary Gear Trains ◽  
Gear Trains ◽  
A New Technique

The most important step in the structural synthesis of planetary gear trains (PGTs) requires the identification of isomorphism (rotational as well as displacement) between the graphs which represent the kinematic structure of planetary gear train. Previously used methods for identifying graph isomorphism yielded incorrect results. Literature review in this area shows there is inconsistency in results from six link, one degree-of-freedom onwards. The purpose of this paper is to present an efficient methodology through the use of Loop concept and Hamming number concept to detect displacement and rotational isomorphism in PGTs in an unambiguous way. New invariants for rotational graphs and displacement graphs called geared chain hamming strings and geared chain loop hamming strings are developed respectively to identify rotational and displacement isomorphism. This paper also presents a procedure to redraw conventional graph representation that not only clarifies the kinematic structure of a PGT but also averts the problem of pseudo isomorphism. Finally a thorough analysis of existing methods is carried out using the proposed technique and the results in the category of six links one degree-of-freedom are established and an Atlas comprises of graph representations in conventional form as well as in new form is presented.

Contrastive Graph Representations for Logical Formulas Embedding

2021 ◽  
pp. 1-1
Author(s):  
Qika Lin ◽  
Jun Liu ◽  
Lingling Zhang ◽  
Yudai Pan ◽  
Xin Hu ◽  
...  
Keyword(s):  
Graph Representations

scholarly journals TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators

2018 ◽  
Vol 35 (15) ◽  
pp. 2686-2689
Author(s):  
Asa Thibodeau ◽  
Dong-Guk Shin
Keyword(s):  
Gene Expression ◽  
Software Tool ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Graph Representations ◽  
Expression Levels ◽  
Conducting Pathway ◽  
Pathway Analysis Tool ◽  
Pathway Analyses ◽  
Gene Expression Levels

Abstract Summary Current approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels. Availability and implementation The TriPOINT open source software is freely available at https://github.uconn.edu/ajt06004/TriPOINT under the GPL v3.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

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