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postgraduate thesis: A systems biology approach for studying human diseases

TitleA systems biology approach for studying human diseases
Authors
Advisors
Issue Date2019
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Zheng, T. [鄭婷婷]. (2019). A systems biology approach for studying human diseases. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
AbstractOver the past few decades with the advance of molecular biology and medicine, the pathogenic mechanisms for more and more diseases have been revealed. Furthermore, some serious illnesses such as hepatitis C virus (HCV), bacterial pneumonia and acute promyelocytic leukemia now have basically become curable. However, for other diseases that are caused by complex risk factors, researchers still cannot fully understand their pathogenesis. Naturally these diseases have high incidence and mortality rate. Thus, a system-level approach is necessary to understand the interactions of a number of molecular processes involved in the disease development and genotype-phenotype relationships in complex diseases. During my PhD studies, I employed different types of ‘omics’ data to explore the risk and progression of diseases from different perspectives. In Chapter II (published in Frontiers in Physiology), by integrating publicly available gene expression profiles for foods and diseases, I identified diet-disease pairs where diet could positively influence disease development and pairs where specific diets should be avoided in a disease state. More importantly, I provide a computational framework for establishing diet-disease associations and additional information on the role of diet in disease development. In Chapter III, I developed VirMiner (published in Microbiome), a user-friendly web tool that employed a random forest model based on actual phagenomics and metagenomics samples to identify phage contigs from metagenomic data. Importantly, VirMiner can predict phage-host interactions, providing novel insights into the effect of phage on pathogenicity. In Chapter IV (the manuscript is in preparation), using paired amplicon sequencing (ITS) and metagenomic data I characterized bacterial and fungal communities in cancer patients before or/and after chemotherapy treatment. Moreover, I investigated the contributions of the specific features on the overgrowth of Candida albicans in human gut, opening new prophylactic avenues for patients at risk. All the aforementioned projects have shown the importance of system biology approaches for facilitating in-depth understanding of human diseases as well as addressing the key issues of biomedicine.
DegreeDoctor of Philosophy
SubjectDiseases - Research
Dept/ProgramBiological Sciences
Persistent Identifierhttp://hdl.handle.net/10722/285972

 

DC FieldValueLanguage
dc.contributor.advisorEl-Nezamy, HS-
dc.contributor.advisorChow, BKC-
dc.contributor.advisorPanagiotou, I-
dc.contributor.authorZheng, Tingting-
dc.contributor.author鄭婷婷-
dc.date.accessioned2020-08-20T04:11:18Z-
dc.date.available2020-08-20T04:11:18Z-
dc.date.issued2019-
dc.identifier.citationZheng, T. [鄭婷婷]. (2019). A systems biology approach for studying human diseases. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/285972-
dc.description.abstractOver the past few decades with the advance of molecular biology and medicine, the pathogenic mechanisms for more and more diseases have been revealed. Furthermore, some serious illnesses such as hepatitis C virus (HCV), bacterial pneumonia and acute promyelocytic leukemia now have basically become curable. However, for other diseases that are caused by complex risk factors, researchers still cannot fully understand their pathogenesis. Naturally these diseases have high incidence and mortality rate. Thus, a system-level approach is necessary to understand the interactions of a number of molecular processes involved in the disease development and genotype-phenotype relationships in complex diseases. During my PhD studies, I employed different types of ‘omics’ data to explore the risk and progression of diseases from different perspectives. In Chapter II (published in Frontiers in Physiology), by integrating publicly available gene expression profiles for foods and diseases, I identified diet-disease pairs where diet could positively influence disease development and pairs where specific diets should be avoided in a disease state. More importantly, I provide a computational framework for establishing diet-disease associations and additional information on the role of diet in disease development. In Chapter III, I developed VirMiner (published in Microbiome), a user-friendly web tool that employed a random forest model based on actual phagenomics and metagenomics samples to identify phage contigs from metagenomic data. Importantly, VirMiner can predict phage-host interactions, providing novel insights into the effect of phage on pathogenicity. In Chapter IV (the manuscript is in preparation), using paired amplicon sequencing (ITS) and metagenomic data I characterized bacterial and fungal communities in cancer patients before or/and after chemotherapy treatment. Moreover, I investigated the contributions of the specific features on the overgrowth of Candida albicans in human gut, opening new prophylactic avenues for patients at risk. All the aforementioned projects have shown the importance of system biology approaches for facilitating in-depth understanding of human diseases as well as addressing the key issues of biomedicine. -
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshDiseases - Research-
dc.titleA systems biology approach for studying human diseases-
dc.typePG_Thesis-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineBiological Sciences-
dc.description.naturepublished_or_final_version-
dc.date.hkucongregation2019-
dc.identifier.mmsid991044146573203414-

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