2015/01/24 by Héctor Zenil, Hector Zenil, Zenil, Hector +4 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bioinformatics and Genomic Networks #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Gene Regulatory Network Analysis #Gene expression and cancer classification #Information Theory (cs.IT) #Spectral Theory (math.SP) #cs.IT #math.DS #math.IT #math.SP
paper · pdf · doi:10.48550/arxiv.1501.06080
Forthcoming in 3rd International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO), Lecture Notes in Bioinformatics, 2015
arxiv created 2015/01/24 · openalex publication_date 2015/01/24 · arxiv updated 2015/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We undertake an extensive numerical investigation of the graph spectra of thousands regular graphs, a set of random Erdös-Rényi graphs, the two most popular types of complex networks and an evolving genetic network by using novel conceptual and experimental tools. Our objective in so doing is to contribute to an understanding of the meaning of the Eigenvalues of a graph relative to its topological and information-theoretic properties. We introduce a technique for identifying the most informative Eigenvalues of evolving networks by comparing graph spectra behavior to their algorithmic complexity. We suggest that extending techniques can be used to further investigate the behavior of evolving biological networks. In the extended version of this paper we apply these techniques to seven tissue specific regulatory networks as static example and network of a naïve pluripotent immune cell in the process of differentiating towards a Th17 cell as evolving example, finding the most and least informative Eigenvalues at every stage.