Simple Pattern-only Heuristics Lead to Fast Subgraph Matching Strategies on Very Large Networks

Practical Applications of Computational Biology and Bioinformatics, 12th International Conference - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-98702-6_16 ◽

2018 ◽

pp. 131-138 ◽

Cited By ~ 1

Author(s):

Antonino Aparo ◽

Vincenzo Bonnici ◽

Giovanni Micale ◽

Alfredo Ferro ◽

Dennis Shasha ◽

...

Keyword(s):

Simple Pattern ◽

Large Networks ◽

Subgraph Matching

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The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees

PLoS ONE ◽

10.1371/journal.pone.0061183 ◽

2013 ◽

Vol 8 (4) ◽

pp. e61183 ◽

Cited By ~ 9

Author(s):

Sofie Demeyer ◽

Tom Michoel ◽

Jan Fostier ◽

Pieter Audenaert ◽

Mario Pickavet ◽

...

Keyword(s):

Search Trees ◽

Large Networks ◽

Matching Algorithm ◽

Subgraph Matching

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Does Globalization Drive a Simple Pattern of Taxation?

SSRN Electronic Journal ◽

10.2139/ssrn.3597755 ◽

2019 ◽

Author(s):

Robert Kudrle

Keyword(s):

Simple Pattern

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Bayesian graphical models for modern biological applications

Statistical Methods & Applications ◽

10.1007/s10260-021-00572-8 ◽

2021 ◽

Author(s):

Yang Ni ◽

Veerabhadran Baladandayuthapani ◽

Marina Vannucci ◽

Francesco C. Stingo

Keyword(s):

Graphical Models ◽

Cancer Genomics ◽

Graph Structure ◽

Biological Processes ◽

Limited Sample ◽

Large Networks ◽

Bayesian Approaches ◽

Complex Sampling ◽

Complex Dependence ◽

Bayesian Graphical Models

AbstractGraphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

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