Integration of Multi-Omic Datasets on Antimicrobial Resistance for Large-Scale Biomedical Data Science
Antimicrobial resistance (AMR) results in tremendous health risks, causing the World Health Organization to designate it as one of the significant burdens for modern society. Owing to ineffective antibiotics, once everyday surgeries will become life-threatening interventions. Rigorous governmental m...
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|Summary:||Antimicrobial resistance (AMR) results in tremendous health risks, causing the World Health Organization to designate it as one of the significant burdens for modern society. Owing to ineffective antibiotics, once everyday surgeries will become life-threatening interventions. Rigorous governmental measurements are supposed to supervise administration of antimicrobials, hence controlling AMR dissemination. The intervention of healthcare stakeholders and responsible application in human and veterinary medicine is urgently required. In this light, wastewater-based epidemiology has been established to examine various environmental factors promoting AMR and monitoring their development population-wide. Antibiotic residuals in human excrements are a significant driver for AMR, and assessing in- and effluent of wastewater treatment plants is evident. Treated wastewater is ultimately released in rivers, lakes, or the sea, elevating AMR from a local to a global health concern. Thus, researchers consider increasingly fresh and salt waters for comprehensive AMR surveys. In this light, recreational waters could be a significant health risk if strained with resistant bacteria. Indeed, freshwater-based epidemiology ascertained hot spots in Asian lakes, underpinning the urgency for timely and consistent AMR surveillance worldwide. However, data consistency is hampered due to a great variety of bioanalytical methods. For this reason, as part of this thesis, we integrated, examined, and evaluated standardized samples from numerous European freshwater lakes. Baseline levels of AMR have been detected, which facilitates future monitoring on a large scale.
The results further emphasized that multi-resistant pathogens require alternative therapeutic options beyond conventional antibiotics. Therefore, scientists study antimicrobial peptides (AMPs). To date, several AMPs advanced in clinical trials or gained market maturity. The success encouraged researchers to develop advanced machine learning (ML) methods for high-throughput AMP screening. However, ML-based integration of peptidomics assumes a machine-readable format, further challenging hyper-parameter optimization. Thus, we explored as part of this thesis the performance concerning encodings, models, and the biomedical domain. Finally, we contributed a novel approach for unsupervised encoding selection and ensemble configuration to this dissertation. In summary, this thesis addresses the collection, analysis, and integration of multi-omic data to pave the way for data-driven research on AMR.|
|Physical Description:||204 Pages|