Life on earth is regulated by a complex system of interactions. Network Medicine models biological organisms through network paradigms allowing researchers to discover and understand the molecular mechanisms that govern biological processes and human diseases. The development of computational methodologies based on the analysis of molecular connections may help, for example, researchers by reducing the time and costs of lab experiments and supporting biomedical advancements in diseases such as cancer, diabetes, and Alzheimer. This thesis focuses on the development of machine learning models that extract information from the human interactome to address crucial problems in biology, medicine, and pharmacology. Four fundamental aspects are explored: protein-protein interactions, gene-disease associations, disease-disease associations, and drug repositioning. As first study presented in Chapter 6 of this work, with the support of a large team of researchers belonging to the Network Medicine Alliance, we conducted a large-scale comparative evaluation of algorithms that predict interactions between proteins for the extension of the fundamental network for Network Medicine, the human interactome. Furthermore, in Chapter 7, we developed RW², a deep learning model applied to the human interactome to identify new gene-disease associations. Subsequently, in Chapter 8, a methodology has been defined to induce a new taxonomy of diseases starting from effective molecules, which integrate existing taxonomies, to identify unexplored relationships between pathologies. Finally, to complete the thesis work and support research on the recent COVID-19 pandemic, in Chapter 9 we present two approaches developed for drug repositioning. The first study combines knowledge of the interactome and pharmacological molecular graphs to predict potential therapeutic targets. The second study, conducted under the supervision of the laboratory directed by Dr. Loscalzo, professor at the Harvard Medical School, aims to understand which biological mechanisms link viruses and drugs.

Machine learning methods for extracting medical knowledge from the human interactome / Madeddu, Lorenzo. - (2022 Jun 09).

Machine learning methods for extracting medical knowledge from the human interactome

MADEDDU, LORENZO
2022

Abstract

Life on earth is regulated by a complex system of interactions. Network Medicine models biological organisms through network paradigms allowing researchers to discover and understand the molecular mechanisms that govern biological processes and human diseases. The development of computational methodologies based on the analysis of molecular connections may help, for example, researchers by reducing the time and costs of lab experiments and supporting biomedical advancements in diseases such as cancer, diabetes, and Alzheimer. This thesis focuses on the development of machine learning models that extract information from the human interactome to address crucial problems in biology, medicine, and pharmacology. Four fundamental aspects are explored: protein-protein interactions, gene-disease associations, disease-disease associations, and drug repositioning. As first study presented in Chapter 6 of this work, with the support of a large team of researchers belonging to the Network Medicine Alliance, we conducted a large-scale comparative evaluation of algorithms that predict interactions between proteins for the extension of the fundamental network for Network Medicine, the human interactome. Furthermore, in Chapter 7, we developed RW², a deep learning model applied to the human interactome to identify new gene-disease associations. Subsequently, in Chapter 8, a methodology has been defined to induce a new taxonomy of diseases starting from effective molecules, which integrate existing taxonomies, to identify unexplored relationships between pathologies. Finally, to complete the thesis work and support research on the recent COVID-19 pandemic, in Chapter 9 we present two approaches developed for drug repositioning. The first study combines knowledge of the interactome and pharmacological molecular graphs to predict potential therapeutic targets. The second study, conducted under the supervision of the laboratory directed by Dr. Loscalzo, professor at the Harvard Medical School, aims to understand which biological mechanisms link viruses and drugs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1639572
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