Semantic Mining based on graph theory and ontologies. Case Study: Cell Signaling Pathways

Abstract

In this paper we use concepts from graph theory and cellular biology represented as ontologies, to carry out semantic mining tasks on signaling pathway networks. Specifically, the paper describes the semantic enrichment of signaling pathway networks. A cell signaling network describes the basic cellular activities and their interactions. The main contribution of this paper is in the signaling pathway research area, it proposes a new technique to analyze and understand how changes in these networks may affect the transmission and flow of information, which produce diseases such as cancer and diabetes. Our approach is based on three concepts from graph theory (modularity, clustering and centrality) frequently used on social networks analysis. Our approach consists into two phases: the first uses the graph theory concepts to determine the cellular groups in the network, which we will call them communities; the second uses ontologies for the semantic enrichment of the cellular communities. The measures used from the graph theory allow us to determine the set of cells that are close (for example, in a disease), and the main cells in each community. We analyze our approach in two cases: TGF-ß and the Alzheimer Disease.

Alzheimer’s disease under the purview of Graph Theory Centric Genetic Networks

“…Network has found its major position in biological concepts that helps in understanding the structural and functional organization of cells. A biological network represents the biological interaction of ecosystem describing relationship between same or different biological species, epidemiology describing spread of infectious diseases world-wide, immune system describing the process that takes places within an organism to protect against foreign antigens, neural networks representing the functional and structural interactions of brain, protein-protein interaction and gene regulation found in cells during DNA replication, transcription and translation processes, metabolic reactions determining the biochemical and physiological reactions inside an organism (Barabasi & Oltvai, 2004;Rangel et al, 2016). Several biology related databases are established to study and unravel the human body and its interplay in terms of cellular functions, molecular components, and genetic interactions.…”

“…Interpretation of high-throughput analysis of genomic data to explore gene regulation and gene expression is of greater interest to the data related to GO and functional association of a set of genes retrieved from biological databases such as Reactome, Kyoto Encyclopedia of Genes and Genomes (KEGG), Pathway commons the intricate direct/indirect interactions among all sorts of macromolecules. Proteins, genes, proteinprotein interactions, and signal transduction mechanism can be well described using network science (Barabasi & Oltvai, 2004;Diao et al, 2007;Rangel et al, 2016;Gene Ontology Consortium, 2006).…”

“…The signalling pathway graph was created based on social network analysis (SNA) for cluster identification. This analysis gives the network of semantically enriched common genes and protein linked to both pathways (Rangel et al, 2016). Similarly, exploiting graph principles such as centrality, modularity, and mining, we might find essential biological pathways, analyze the functional similarities between gene communities, and build protein interaction networks.…”