Network Analysis and Machine Learning Assist Drug Repurposing and Safety Assessment in Neurological Diseases - PhD thesis final exam report

Aug 5, 2026

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In recent decades, network analysis and machine learning have become key approaches for data modeling. This PhD thesis develops practical methods for drug research by combining network theory, computational techniques, and biomedical evidence. Across six projects, the work increases model complexity while focusing on drug repurposing and safety in neurological diseases. Outcomes include new algorithms, a SARS-CoV-2 clinical research tracking web application, a Python package for differential gene expression and switch-gene detection, identification of medication-linked impulsivity events, an automated repurposing discovery pipeline, and a knowledge graph with graph-based prediction models.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages223

Reading Time9 h 22 min

AccessFree to download

Introduction

Thesis Overview

Network Theory

Machine Learning

Networks in Drug Research

Aim of the Work

Projects

Projects Overview

Descriptive Models

Predictive Models

Data Analysis

ALMA MATER STUDIORUM · UNIVERSITÀ DI BOLOGNA DOTTORATO DI RICERCA IN DATA SCIENCE AND COMPUTATION Ciclo XXXV Settore Concorsuale: 03/D1-Chimica e Tecnologie Farmaceutiche, Tossicologiche e Nutraceutico-alimentari Settore Scientifico Disciplinare: CHIM/08-Chimica Farmaceutica Network Analysis and Machine Learning Assist Drug Repurposing and Safety Assessment in Neurological Diseases Presentata da: Luca Menestrina Coordinatore Dottorato Prof. Daniele Bonacorsi Supervisore Prof. Maurizio Recanatini Co-supervisore Prof. Andrea Cavalli Esame Finale Anno 2024 Alla mia Famiglia Abstract In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2 . • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities. Table of Contents List of Acronyms and Abbreviations VII I Introduction 1 0 Thesis Overview 3 1 Network Theory 5 1.1 Basic Concepts ............................. 5 1.2 Graph Properties ............................ 9 1.3 Network-based Predictions . . . . . . . . . . . . . . . . . . . . . . . 20 2 Machine Learning 23 2.1 Learning Techniques .......................... 24 2.2 Graph Machine Learning ........................ 26 3 Networks in Drug Research 29 3.1 Network Biology, Pharmacology, and Medicine ............ 29 3.2 Network Types ............................. 30 3.3 Network Applications .......................... 34 4 Aim of the Work 39 II Projects 41 5 Projects Overview 43 6 Descriptive Models 45 6.1 COVIDrugNet .............................. 45 6.1. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.1.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . 50 6.1.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.1.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.1.5 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.2 DEGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.2. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.2.3 Results and Discussion . . . . . . . . . . . . .

FAQs

What is the main goal of the thesis?

To apply network analysis and machine learning to drug research, with specific emphasis on drug repurposing and safety in neurological diseases.

What are the thesis projects’ key outputs?

They include a computational method for adverse events in fixed-dose combinations, a SARS-CoV-2 clinical research response web application, a Python package for differential gene expression and switch-gene identification, and additional pipelines and predictive models such as knowledge graphs.

How does the thesis approach drug research complexity?

It presents six projects whose modeling complexity increases progressively, ranging from descriptive models to predictive models and broader data analysis workflows.

Aug 5, 2026

Upload by Maeve