UNCOVERING THE MUTATIONAL LANDSCAPE OF SARS-COV-2 USING MACHINE LEARNING METHODS

Aug 5, 2026

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The thesis investigates the mutational landscape of SARS-CoV-2 through machine learning methods, focusing on how evolutionary capacity can be inferred from mutation patterns. It introduces a research framework that connects mutational signature dynamics to selective pressures from host antiviral molecules. The work further extends modelling approaches to characterize viral proteins and analyze co-occurrence relationships among mutations, supported by detailed data and methodological design.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages239

Reading Time10 h 2 min

AccessFree to download

Introduction

Virology

Machine Learning

Mutational signature dynamics indicate SARS-CoV-2’s evolutionary capacity is driven by host antiviral molecules

Abstract

Summary

Methods

Lamb, Kieran Daniel (2024) Uncovering the mutational landscape of SARSCoV-2 using machine learning methods. PhD thesis. [https://theses.gla.ac.uk/84637/](https://theses.gla.ac.uk/84637/) Copyright and moral rights for this work are retained by the author A copy can be downloaded for personal non-commercial research or study, without prior permission or charge This work cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given Enlighten: Theses [https://theses.gla.ac.uk/](https://theses.gla.ac.uk/) [research-enlighten@glasgow.ac.uk](research-enlighten@glasgow.ac.uk) UNCOVERING THE MUTATIONAL LANDSCAPE OF SARS-COV-2 USING MACHINE LEARNING METHODS KIERAN DANIEL LAMB Submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy College of Medical, Veterinary and Life Sciences University of Glasgow 1 TABLE OF CONTENTS Table of Contents .............................................................................................. 1 Figures List ....................................................................................................... 5 Tables List ...................................................................................................... 12 Abbreviations ...................................................................................................13 Acknowledgements .......................................................................................... 15 Declaration ..................................................................................................... 18 Abstract ......................................................................................................... 19 1 Introduction .............................................................................................. 21 1.1 Virology ............................................................................................... 23 1.1.1 The Baltimore System ......................................................................................................24 1.1.2 Virus Taxonomy and Evolution ........................................................................................ 26 1.1.3 Phylogenetics ................................................................................................................... 27 1.1.4 Sequence Alignment ........................................................................................................28 1.1.5 Phylogenetic Methods..................................................................................................... 29 1.1.6 SARS-CoV-2 ..................................................................................................................... 33 1.2 Machine Learning ................................................................................. 44 1.2.1 Signals in the sequences .................................................................................................. 44 1.2.2 Supervised Machine Learning.......................................................................................... 46 1.2.3 Unsupervised Machine Learning.......................................................................................48 1.2.4 Self-supervised Machine Learning ................................................................................... 49 1.2.5 Deep learning ...................................................................................................................50 1.2.6 Biological sequence embeddings......................................................................................58 1.2.7 Dimensionality Reductio

FAQs

What is the main research focus of this thesis?

The thesis focuses on uncovering the mutational landscape of SARS-CoV-2 using machine learning methods.

How does the thesis relate mutational patterns to viral evolution?

It uses mutational signature dynamics to indicate that the virus’s evolutionary capacity is driven by host antiviral molecules.

Which machine learning approaches are covered in the thesis background?

The introduction includes supervised, unsupervised, self-supervised learning, deep learning, biological sequence embeddings, and methods for dimensionality reduction and source separation.

Aug 5, 2026

Upload by Violet