Artificial Intelligence Techniques for Colorectal Cancer Drug Metabolism: Ontologies and Complex Networks

Marcos      Martinez-Romero; Jose M.      Vazquez-Naya; Juan      R. Rabunal; Salvador      Pita-Fernandez; Ramiro      Macenlle; Javier      Castro-Alvarino; Leopoldo      Lopez-Roses; Jose      L. Ulla; Antonio      V. Martinez-Calvo; Santiago      Vazquez; Javier      Pereira; Ana B.      Porto-Pazos; Julian      Dorado; Alejandro      Pazos; Cristian      R. Munteanu

doi:10.2174/138920010791514289

Abstract

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process subontology from the Gene Ontology.

Keywords: Ontology, graphs, colorectal cancer, complex network, drug metabolic pathway

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