{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132547"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132547","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Integrative approaches to decipher influenza evolution, antibody responses, and AI-driven specificity prediction","abstract":"The ongoing threat of viral pathogens, such as SARS-CoV-2 and influenza Viruses, highlights the urgent need to understand immune responses and viral evolution to guide therapeutic and vaccine development. This dissertation integrates high-throughput experimental techniques and artificial intelligence (AI) to address key questions in virus-immunity interactions through three interconnected research areas: (1) deep mutational scanning (DMS) to map sequence–function relationships in influenza viral proteins; (2) large-scale analysis of antibody responses to SARS-CoV-2 and influenza; and (3) development of AI models to predict antibody specificity. Chapter 1 introduces the rapid advancement of high-throughput and AI methodologies for studying immune responses and viral evolution. Chapter 2 presents a robust DMS pipeline that reveals high N-terminal tolerance in the nuclear export protein (NEP) and identifies charge-driven epistasis as a constraint on neuraminidase (NA) antigenic evolution. Chapter 3 describes large-scale profiling of antibody repertoires across viral pathogens, identifying critical residues in IGHV1-69 broadly neutralizing antibodies that target the hemagglutinin (HA) stem. Chapter 4 showcases AI-driven models for predicting antibody specificity and highlights their promise for therapeutic design. Chapter 5 synthesizes key findings and outlines future directions. By combining high-throughput experimentation with AI, this dissertation advances our understanding of host–pathogen interactions and provides new tools for vaccine design and immunotherapy.","abstract_html":"The ongoing threat of viral pathogens, such as SARS-CoV-2 and influenza Viruses, highlights the urgent need to understand immune responses and viral evolution to guide therapeutic and vaccine development. This dissertation integrates high-throughput experimental techniques and artificial intelligence (AI) to address key questions in virus-immunity interactions through three interconnected research areas: (1) deep mutational scanning (DMS) to map sequence–function relationships in influenza viral proteins; (2) large-scale analysis of antibody responses to SARS-CoV-2 and influenza; and (3) development of AI models to predict antibody specificity. Chapter 1 introduces the rapid advancement of high-throughput and AI methodologies for studying immune responses and viral evolution. Chapter 2 presents a robust DMS pipeline that reveals high N-terminal tolerance in the nuclear export protein (NEP) and identifies charge-driven epistasis as a constraint on neuraminidase (NA) antigenic evolution. Chapter 3 describes large-scale profiling of antibody repertoires across viral pathogens, identifying critical residues in IGHV1-69 broadly neutralizing antibodies that target the hemagglutinin (HA) stem. Chapter 4 showcases AI-driven models for predicting antibody specificity and highlights their promise for therapeutic design. Chapter 5 synthesizes key findings and outlines future directions. By combining high-throughput experimentation with AI, this dissertation advances our understanding of host–pathogen interactions and provides new tools for vaccine design and immunotherapy.","abstract_has_math":false,"creators":["Wang, Yiquan"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biochemistry","degree_department":null,"school":null,"contributors":["Wu, Nicholas","Brooke, Christopher","Stadtmueller, Beth","Tajkhorshid, Emad"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Deep learning","Antibody Recognition","Influenza Virus","Artificial Intelligence"],"languages":["en"],"rights":["© 2025 Yiquan Wang. All rights reserved"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132547","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wu, Nicholas","Brooke, Christopher","Stadtmueller, Beth","Tajkhorshid, Emad"]},{"key":"dc:creator","label":"Author","values":["Wang, Yiquan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-02"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biochemistry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning","Antibody Recognition","Influenza Virus","Artificial Intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2025 Yiquan Wang. All rights reserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132547"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The ongoing threat of viral pathogens, such as SARS-CoV-2 and influenza Viruses, highlights the urgent need to understand immune responses and viral evolution to guide therapeutic and vaccine development. This dissertation integrates high-throughput experimental techniques and artificial intelligence (AI) to address key questions in virus-immunity interactions through three interconnected research areas: (1) deep mutational scanning (DMS) to map sequence–function relationships in influenza viral proteins; (2) large-scale analysis of antibody responses to SARS-CoV-2 and influenza; and (3) development of AI models to predict antibody specificity. Chapter 1 introduces the rapid advancement of high-throughput and AI methodologies for studying immune responses and viral evolution. Chapter 2 presents a robust DMS pipeline that reveals high N-terminal tolerance in the nuclear export protein (NEP) and identifies charge-driven epistasis as a constraint on neuraminidase (NA) antigenic evolution. Chapter 3 describes large-scale profiling of antibody repertoires across viral pathogens, identifying critical residues in IGHV1-69 broadly neutralizing antibodies that target the hemagglutinin (HA) stem. Chapter 4 showcases AI-driven models for predicting antibody specificity and highlights their promise for therapeutic design. Chapter 5 synthesizes key findings and outlines future directions. By combining high-throughput experimentation with AI, this dissertation advances our understanding of host–pathogen interactions and provides new tools for vaccine design and immunotherapy.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Yiquan Wang, accepted the attached license on 2025-12-01 at 00:43.","The student, Yiquan Wang, submitted this Dissertation for approval on 2025-12-01 at 01:02.","This Dissertation was approved for publication on 2025-12-02 at 09:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22987 on 2026-02-19 at 18:25:45"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Integrative approaches to decipher influenza evolution, antibody responses, and AI-driven specificity prediction"]}]}],"canonical_facts":{"dc:contributor":["Wu, Nicholas","Brooke, Christopher","Stadtmueller, Beth","Tajkhorshid, Emad"],"dc:creator":["Wang, Yiquan"],"dc:date":["2025-12","2025-12-02"],"dc:description":["The ongoing threat of viral pathogens, such as SARS-CoV-2 and influenza Viruses, highlights the urgent need to understand immune responses and viral evolution to guide therapeutic and vaccine development. This dissertation integrates high-throughput experimental techniques and artificial intelligence (AI) to address key questions in virus-immunity interactions through three interconnected research areas: (1) deep mutational scanning (DMS) to map sequence–function relationships in influenza viral proteins; (2) large-scale analysis of antibody responses to SARS-CoV-2 and influenza; and (3) development of AI models to predict antibody specificity. Chapter 1 introduces the rapid advancement of high-throughput and AI methodologies for studying immune responses and viral evolution. Chapter 2 presents a robust DMS pipeline that reveals high N-terminal tolerance in the nuclear export protein (NEP) and identifies charge-driven epistasis as a constraint on neuraminidase (NA) antigenic evolution. Chapter 3 describes large-scale profiling of antibody repertoires across viral pathogens, identifying critical residues in IGHV1-69 broadly neutralizing antibodies that target the hemagglutinin (HA) stem. Chapter 4 showcases AI-driven models for predicting antibody specificity and highlights their promise for therapeutic design. Chapter 5 synthesizes key findings and outlines future directions. By combining high-throughput experimentation with AI, this dissertation advances our understanding of host–pathogen interactions and provides new tools for vaccine design and immunotherapy.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Yiquan Wang, accepted the attached license on 2025-12-01 at 00:43.","The student, Yiquan Wang, submitted this Dissertation for approval on 2025-12-01 at 01:02.","This Dissertation was approved for publication on 2025-12-02 at 09:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22987 on 2026-02-19 at 18:25:45"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132547"],"dc:language":["en"],"dc:rights":["© 2025 Yiquan Wang. All rights reserved"],"dc:subject":["Deep learning","Antibody Recognition","Influenza Virus","Artificial Intelligence"],"dc:title":["Integrative approaches to decipher influenza evolution, antibody responses, and AI-driven specificity prediction"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Biochemistry"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}