10/11/2017 - Integrative Directed Cyclic Graphical Models with Heterogeneous Samples - Palestrante: PhD Yang Ni (The University of Texas at Austin)

Seminário Conjunto UFSCar/ICMC – 10/11/2017 - 14h00

LOCAL: Sala 43, DEs-UFSCar

PALESTRANTE: PhD Yang Ni, Department of Statistics and Data Sciences, The University of Texas at Austin

TÍTULO: Integrative Directed Cyclic Graphical Models with Heterogeneous Samples

RESUMO: In this talk, I will introduce novel hierarchical directed cyclic graphical models to infer gene networks by integrating genomic data across platforms and across diseases. The proposed model takes into account tumor heterogeneity. In the case of data that can be naturally divided into known groups, we propose to connect graphs by
introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs. Thresholding priors are applied to induce sparsity of the estimated networks. In the case of unknown groups, we cluster subjects into subpopulations and jointly estimate cluster-specific gene networks, again using similar hierarchical
priors across clusters. Two applications with multiplatform genomic data for multiple cancers will be presented to illustrate the utility of our model.

ATENÇÃO: O seminário será via internet e os organizadores antecipam as desculpas por eventuais contratempos de natureza técnica, mas caso seja bem sucedido, o seminário será uma excelente oportunidade de atualização no tema “Modelos Grafos”, uma poderosa ferramenta de modelagem e o nosso palestrista YANG NI é um especialista no assunto e foi o ganhador do Savage Award (Honorable Mention), com a tese “Bayesian Graphical Models for Complex Biological Networks”, na Rice University.

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