Molecules to medicine: advancing integrated computational frameworks for biomedical applications

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dc.contributor.advisorTabirca, Marius-Sabin
dc.contributor.advisorO'Reilly, Barry A.
dc.contributor.authorMi, Yanlinen
dc.contributor.funderResearch Ireland
dc.date.accessioned2026-01-19T14:27:25Z
dc.date.available2026-01-19T14:27:25Z
dc.date.issued2025
dc.date.submitted2025
dc.description.abstractWith the increasing complexity of biomedical knowledge and its interdisciplinary nature, effective translation of knowledge faces significant challenges. From molecular discovery to therapeutic application, it is challenging to ensure conceptual continuity and operational consistency due to the fragmented and domain-specific nature of current technology. In this thesis, we propose a unified computational framework, Translation-as-Infrastructure, to systematically support the stable migration of biomedical knowledge across cognitive levels, data forms, and operating systems. Guided by the principles of semantic fidelity, structural unity, and output interpretability, the framework addresses four major translational bottlenecks: translating patient narratives into structured system logic, translating heterogeneous clinical data into trustworthy diagnostic reasoning, translating molecular insights into clinically actionable knowledge, and translating protein properties into scalable, computable representations. To operationalize this framework, some specific systems and computational tools have been developed and validated, including patient service platforms (MSaaVS, PHPlace), data modeling systems (Strengthening Diagnostic Translation), cross-level inference models (EPOP, AI-driven protein function analysis), and a modular protein computing infrastructure (PROFASA, PS-GO, Silver Surfer, ProteinFlow). By integrating these systems, the thesis constructs a continuous, multi-layered translational pipeline from molecules to medicine. While this work represents an initial foundational effort, primarily focusing on structured knowledge migration rather than fully addressing semantic transparency, causal inference, and dynamic system self-adaptation, it provides a scalable architecture for future development of adaptive biomedical knowledge systems. By rethinking translation as a computational and infrastructural mechanism, this study develops a new paradigm for multidisciplinary knowledge engineering, providing methodological improvements and practical tools to assist the next generation of biomedical informatics and translational research.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMi, Y. 2025. Molecules to medicine: advancing integrated computational frameworks for biomedical applications. PhD Thesis, University College Cork.
dc.identifier.endpage286
dc.identifier.urihttps://hdl.handle.net/10468/18409
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.rights© 2025, Yanlin Mi.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectProtein design
dc.subjectPelvic organ prolapse
dc.subjectMachine learning
dc.subjectExtracellular matrix biomarkers
dc.subjectSurgical outcome prediction
dc.subjectTelemedicine
dc.subjectSoftware-as-a-service
dc.subjectHealthcare accessibility
dc.subjectComputational biology
dc.subjectMolecular visualisation
dc.subjectData preprocessing
dc.titleMolecules to medicine: advancing integrated computational frameworks for biomedical applications
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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