Note on Pertinence of Graph Theory for Genetic Networks
V. Yegnanarayanan
Published: August 01, 2021.
Abstract
Data collection process has witnessed explosive growth that could even outsmart the measurable gains in the field of power of computation as speculated by Moore’s Law. However, scientists spread over various domain are smearing victory through network models to denote their data. Graph- algorithms come in handy to investigate the topological structure and bring out the hidden relationships and attributes among variables. But the task of finding dense subgraphs, are still hard to negotiate from a computational viewpoint. So, it is stimulating to find new algorithmic methods in the probe of graphs derived from huge data sources that are complex. We provide a bird’s eye view of application of graph theoretic tools crisply for the probe of complex networks based on gene expression signatures and to find exact and effective solutions to hard combinatorial tasks such as epigenetic biomarker detection. To make an efficient design which is algorithm based, a probe of two core components of fixed parameter tractability namely branching and kernelization is suggested for parallel imposition of vertex cover. We have also discussed the concept of Para clique and domination concept to better handle gene expression networks.
Keywords: Genes; Genetic Networks; Data Types; Graphs; Clique Domination