2014/05/06 by Peter Hinow, Hinow, Peter, Edward A. Rietman +4
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #FOS: Biological sciences #Microbial Metabolic Engineering and Bioproduction #Molecular Networks (q-bio.MN) #q-bio.MN
paper · pdf · doi:10.48550/arxiv.1405.1462
15 pages, 4 figures
arxiv created 2014/05/06 · openalex publication_date 2014/05/06 · arxiv updated 2014/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Protein-protein interaction networks associated with diseases have gained prominence as an area of research. We investigate algebraic and topological indices for protein-protein interaction networks of 11 human cancers derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a strong correlation between relative automorphism group sizes and topological network complexities on the one hand and five year survival probabilities on the other hand. Moreover, we identify several protein families (e.g. PIK, ITG, AKT families) that are repeated motifs in many of the cancer pathways. Interestingly, these sources of symmetry are often central rather than peripheral. Our results can aide in identification of promising targets for anti-cancer drugs. Beyond that, we provide a unifying framework to study protein-protein interaction networks of families of related diseases (e.g. neurodegenerative diseases, viral diseases, substance abuse disorders).