Complex Graphical Models for Biological Network Science


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Project Description

This project concerns the development of novel principled statistical tools for the analysis of complex networks under non-standard experimental setups.

The methodological innovations are achieved with this proposal are as follows:

  • Development of multiple, paired, and covariate-dependent graphical models for heterogeneous networks;
  • Development of single and multiple graphical models for causal inference based on observational and interventional data;
  • Development of graphical models for non-normal and censored random variables.

This projects is developed by the collaborative efforts of four research units based at the University of Florence (UNIFI), Università Cattolica del Sacro Cuore, (UCSC), University of Padova (UNIPD), and University of Palermo (UNIPA).

Principal Investigator

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Francesco C. Stingo

Associate Professor

Local Coordinators

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Francesco C. Stingo

Associate Professor

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Alberto Roverato

Full Professor

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Luigi Augugliaro

Full Professor

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Guido Consonni

Full Professor

University of Florence

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Francesco C. Stingo

Associate Professor

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Anna Gottard

Associate Professor

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Luca La Rocca

Associate Professor

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Monia Lupparelli

Associate Professor

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Agnese Panzera

Associate Professor

Claudio Busatto

Postdoctoral Researcher

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Giovanni Poli

PhD Student

University of Padua

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Alberto Roverato

Full Professor

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Mauro Bernardi

Associate Professor

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Manuela Cattelan

Associate Professor

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Veronica Vinciotti

Associate Professor

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Nicolas Bianco

Postdoctoral Researcher

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Cristian Castiglione

Postdoctoral Researcher

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Dung Ngoc Nguyen

Postdoctoral Researcher

Università Cattolica del Sacro Cuore

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Guido Consonni

Full Professor

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Lucia Paci

Associate Professor

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Stefano Peluso

Associate Professor

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Federico Castelletti

Assistant Professor

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Francesco Denti

Assistant Professor

University of Palermo

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Luigi Augugliaro

Full Professor

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Angelo M. Mineo

Full Professor

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Antonino Abbruzzo

Associate Professor

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Chiara Di Maria

Assistant Professor

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Rita Fici

PhD Student