Priorización de genes y búsqueda de fármacos por medio de herramientas informáticas y técnicas de aprendizaje de máquinas en osteosarcoma

Abstract

Osteosarcoma is the most common subtype of primary bone cancer, affecting mainly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has not yet been accurately determined. On the other hand, the clinical diagnosis is generalist and therapies have not changed in recent decades. Although nowadays 5-year survival rates can reach up to 60-70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. Thus, the objective of this doctoral thesis is to develop a prioritization strategy that allows the identification of genes associated with the pathogenicity of osteosarcoma, and to explain more fully the etiology of this disease. On the other hand, it seeks to develop drug prediction algorithms based on machine learning techniques that allow proposing new therapeutic agents for the treatment of this disease. All the results obtained in this research were published in international scientific journals with an important JCR impact factor.