Comparative evaluation of network reconstruction methods in high dimensional settings

• Ano de defesa: 2017
• Autor(a) principal: Bolfarine, Henrique
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Correlação parcial; Gaussian Graphical Model; GGMridge; GLasso; Graphical model; LPC; Modelos gráficos; Modelos Gráficos Gaussianos; Network Reconstruction; Partial correlation; Reconstrução de redes
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/45/45133/tde-18052017-001105/

In the past years, several network reconstruction methods modeled as Gaussian Graphical Model in high dimensional settings where proposed. In this work we will analyze three different methods, the Graphical Lasso (GLasso), Graphical Ridge (GGMridge) and a novel method called LPC, or Local Partial Correlation. The evaluation will be performed in high dimensional data generated from different simulated random graph structures (Erdos-Renyi, Barabasi-Albert, Watts-Strogatz ), using Receiver Operating Characteristic or ROC curve. We will also apply the methods in the reconstruction of genetic co-expression network for the differentially expressed genes in cervical cancer tumors.