Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning
Background: The pulmonary microbiome plays a crucial role in chronic respi- ratory diseases. However, the translation of mathematical approaches for clinical value remains limited. This thesis aims to bridge the gap between mathematical techniques and clinical sciences to explore the potential of th...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/173817 |
| _version_ | 1811688913829363712 |
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| author | Jayanth Kumar Narayana |
| author2 | Sanjay Haresh Chotirmall |
| author_facet | Sanjay Haresh Chotirmall Jayanth Kumar Narayana |
| author_sort | Jayanth Kumar Narayana |
| collection | NTU |
| description | Background: The pulmonary microbiome plays a crucial role in chronic respi- ratory diseases. However, the translation of mathematical approaches for clinical value remains limited. This thesis aims to bridge the gap between mathematical techniques and clinical sciences to explore the potential of the pulmonary micro- biome for precision medicine. Results: Integrative-microbiomics (https://integrative- microbiomics.ntu.edu.sg), a novel approach integrating multiple microbiomes, en- hances patient stratification in bronchiectasis. Microbial association networks (in- teractome) and its network-based metrics outperform microbial abundance alone
in associating with clinical outcomes, and identifies a dysregulated ‘gut-lung” axis in high-risk bronchiectasis. A novel application of Compositional Data Analysis (CoDA) to the pulmonary microbiome in COPD reveals time-dependent effects of antibiotics not captured by traditional microbiome analysis. Conclusion: This thesis highlights the essential role of innovative mathematical techniques in pul- monary microbiome analysis. It contributes to data-integration, network science, compositional data analysis, patient stratification, and clinical interventions for chronic respiratory diseases. |
| first_indexed | 2024-10-01T05:39:46Z |
| format | Thesis-Doctor of Philosophy |
| id | ntu-10356/173817 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:39:46Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1738172024-03-07T08:52:06Z Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning Jayanth Kumar Narayana Sanjay Haresh Chotirmall Lee Kong Chian School of Medicine (LKCMedicine) Krasimira Tsaneva-Atanasova schotirmall@ntu.edu.sg Mathematical Sciences Medicine, Health and Life Sciences Microbiome Data integration Lung diseases Machine learning Metagenomics Background: The pulmonary microbiome plays a crucial role in chronic respi- ratory diseases. However, the translation of mathematical approaches for clinical value remains limited. This thesis aims to bridge the gap between mathematical techniques and clinical sciences to explore the potential of the pulmonary micro- biome for precision medicine. Results: Integrative-microbiomics (https://integrative- microbiomics.ntu.edu.sg), a novel approach integrating multiple microbiomes, en- hances patient stratification in bronchiectasis. Microbial association networks (in- teractome) and its network-based metrics outperform microbial abundance alone in associating with clinical outcomes, and identifies a dysregulated ‘gut-lung” axis in high-risk bronchiectasis. A novel application of Compositional Data Analysis (CoDA) to the pulmonary microbiome in COPD reveals time-dependent effects of antibiotics not captured by traditional microbiome analysis. Conclusion: This thesis highlights the essential role of innovative mathematical techniques in pul- monary microbiome analysis. It contributes to data-integration, network science, compositional data analysis, patient stratification, and clinical interventions for chronic respiratory diseases. Doctor of Philosophy 2024-02-29T03:37:15Z 2024-02-29T03:37:15Z 2024 Thesis-Doctor of Philosophy Jayanth Kumar Narayana (2024). Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173817 https://hdl.handle.net/10356/173817 10.32657/10356/173817 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Mathematical Sciences Medicine, Health and Life Sciences Microbiome Data integration Lung diseases Machine learning Metagenomics Jayanth Kumar Narayana Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning |
| title | Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning |
| title_full | Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning |
| title_fullStr | Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning |
| title_full_unstemmed | Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning |
| title_short | Debugging lung diseases: applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling, data integration and machine learning |
| title_sort | debugging lung diseases applying mathematical techniques for precision medicine on the pulmonary microbiome through modelling data integration and machine learning |
| topic | Mathematical Sciences Medicine, Health and Life Sciences Microbiome Data integration Lung diseases Machine learning Metagenomics |
| url | https://hdl.handle.net/10356/173817 |
| work_keys_str_mv | AT jayanthkumarnarayana debugginglungdiseasesapplyingmathematicaltechniquesforprecisionmedicineonthepulmonarymicrobiomethroughmodellingdataintegrationandmachinelearning |