Abstract
Precision medicine is an emerging approach to healthcare that tailors prevention and treatment strategies by accounting for individual patient variability. Its implementation is becoming more feasible due to advances in the scalability and cost-effectiveness of various molecular profiling technologies, such as genomics, transcriptomics, proteomics, and metabolomics. These advances have expanded not only the amount of molecular data measurable from individuals but also the availability of large-scale datasets for research, creating opportunities to discover more effective treatments, identify disease biomarkers, and develop models for predicting disease risk. However, the sheer volume and complexity of these data necessitate advanced computational methods to extract meaningful and actionable insights for precision medicine. This dissertation develops and applies computational frameworks to address various aspects of precision medicine, including predicting the effects of drug combination treatments, utilizing metabolomic biomarkers in disease risk assessment, and improving the methodological aspects of disease risk prediction. The first publication presents a machine learning framework designed to predict the effects of drug combinations across varying doses, providing an improvement over existing methods by enabling precise dose-specific predictions. This method achieved highly accurate predictions and identified novel drug combination synergies, which were subsequently experimentally validated. This framework provides an efficient tool for systematic pre-screening of drug combinations, particularly to advance cancer treatments. The second set of publications expands the current understanding of blood biomarkers in disease risk prediction through the analysis of population-scale metabolomic data. These studies identified novel metabolomic biomarkers and highlighted their potential in predicting the risks of various diseases, including diseases where metabolomics had not previously been studied at scale. The final publication proposes a machine learning method aimed at improving time-toevent disease risk prediction by incorporating comprehensive interaction effects among predictor variables. This method demonstrated improved accuracy in risk prediction compared to standard methods across multiple diseases and different data sources, thereby supporting the development of more accurate tools for risk assessment. Taken together, the novel methods and biological insights presented in this dissertation advance the translation of molecular data into prevention and treatment strategies in precision medicine.
Translated title of the contribution | Koneoppimisratkaisuja täsmälääketieteeseen |
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Original language | English |
Qualification | Doctor's degree |
Awarding Institution |
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Supervisors/Advisors |
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Publisher | |
Print ISBNs | 978-952-64-2351-7 |
Electronic ISBNs | 978-952-64-2352-4 |
Publication status | Published - 2025 |
MoE publication type | G5 Doctoral dissertation (article) |
Keywords
- precision medicine
- machine learning
- predictive modelling
- survival
- analysis
- risk prediction
- metabolomics
- drug combinations