Predicting Mutational Pathways of Influenza A H1N1 Virus using Q-learning

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dc.contributor.authorAarathi Raghuraman, FNUen
dc.contributor.committeechairHeath, Lenwood S.en
dc.contributor.committeememberVinatzer, Boris A.en
dc.contributor.committeememberMurali, T. M.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2023-02-05T07:00:12Zen
dc.date.available2023-02-05T07:00:12Zen
dc.date.issued2021-08-13en
dc.description.abstractInfluenza is a seasonal viral disease affecting over 1 billion people annually around the globe, as reported by the World Health Organization (WHO). The influenza virus has been around for decades causing multiple pandemics and encouraging researchers to perform extensive analysis of its evolutionary patterns. Current research uses phylogenetic trees as the basis to guide population genetics and other phenotypic characteristics when describing the evolution of the influenza genome. Phylogenetic trees are one form of representing the evolutionary trends of sequenced genomes, but that do not capture the multidimensional complexity of mutational pathways. We suggest representing antigenic drifts within influenza A/H1N1 hemagglutinin (HA) protein as a graph, $G = (V, E)$, where $V$ is the set of vertices representing each possible sequence and $E$ is the set of edges representing single amino acid substitutions. Each transition is characterized by a Malthusian fitness model incorporating the genetic adaptation, vaccine similarity, and historical epidemiological response using mortality as the metric where available. Applying reinforcement learning with the vertices as states, edges as actions, and fitness as the reward, we learn the high likelihood mutational pathways and optimal policy, without exploring the entire space of the graph, $G$. Our average predicted versus actual sequence distance of $3.6 \pm 1.2$ amino acids indicates that our novel approach of using naive Q-learning can assist with influenza strain predictions, thus improving vaccine selection for future disease seasons.en
dc.description.abstractgeneralInfluenza is a seasonal virus affecting over 1 billion people annually around the globe, as reported by the World Health Organization (WHO). The effectiveness of influenza vaccines varies tremendously by the type (A, B, C or D) and season. Of note is the pandemic of 2009, where the influenza A H1N1 virus mutants were significantly different from the chosen vaccine composition. It is pertinent to understand and predict the underlying genetic and environmental behavior of influenza virus mutants to be able to determine the vaccine composition for future seasons, preventing another pandemic. Given the recent 2020 COVID-19 pandemic, which is also a virus that affects the upper respiratory system, novel approaches to predict viruses need to be investigated now more than ever. Thus, in this thesis, I develop a novel approach to predicting a portion of the influenza A H1N1 viruses using machine learning.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:32165en
dc.identifier.urihttp://hdl.handle.net/10919/113672en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectFitnessen
dc.subjectMutational Pathwaysen
dc.subjectMutational Pathsen
dc.subjectReinforcement Learningen
dc.subjectQ-Learningen
dc.titlePredicting Mutational Pathways of Influenza A H1N1 Virus using Q-learningen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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