Leveraging interpretable machine learning for multiomic and clinical data integration in biomarker discovery for precision medicine

Nguyen, Phong Ba Hung (2025): Leveraging interpretable machine learning for multiomic and clinical data integration in biomarker discovery for precision medicine. Dissertation, LMU München: Fakultät für Biologie

Abstract

Precision medicine represents a pivotal advancement in healthcare, offering the potential to tailor treatments based on individual molecular, environmental, and lifestyle factors, thus improving patient outcomes and reducing adverse effects. However, significant challenges remain, including the underutilization of different modalities of data, data sparsity, issues with interpretability, and the need for sophisticated computational methods to analyze high-dimensional datasets. To address these challenges, this thesis introduces multiple advancements in methodology for various aspects of precision medicine, from preclinical to clinical studies. For preclinical studies such as drug high throughput screens of cancer cell lines, the stratification with inferred ancestry information enhanced biomarker discovery, demonstrating improved identification of drug response biomarkers. In addition, for observational clinical studies, integration of multiomic data within a biologically interpretable framework, providing an end-to-end comprehensive and transparent machine learning approach to biomarker discovery for complex metabolic diseases. Furthermore, for more complex data such as clinical longitudinal electronic health records, the utilization of pretrained large language models to develop an interpretable prognostic model for type 2 diabetes offered valuable insights into disease progression and patient management. Together, these proposed methods highlight the transformative potential of integrating advanced machine learning techniques and diverse data types to advance biomarker discovery in multiple aspects of precision medicine. Insights into disease mechanisms and actionable biomarkers discovered from these studies serve as valuable resources to help translate both biomedical research and healthcare practice and eventually benefit patients.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Themengebiete: 500 Naturwissenschaften und Mathematik ; 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
• Fakultäten: Fakultät für Biologie
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 12. März 2025
• 1. Bericht­erstatter:in: Menden, Michael