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Title: Elucidating new molecular drivers and pathways involved in Alzheimer's disease using systems biology approaches
Author: Zafeiris, Dimitrios
ISNI:       0000 0004 7965 9130
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2019
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Alzheimer's Disease is the most common form of dementia worldwide with 40 million patients in the USA alone. This neurodegenerative disease is commonly characterised by the presence of amyloid plaques and neurofibrillary tangles in the brain, which result from the deposition of extracellular β-amyloid protein fragments and abnormal tau protein respectively. Over the years, research and medical efforts to control the disease by targeting these proteins have been largely unsuccessful, originally due to the difficulty in detection and targeting, but even with advanced technology, the effects of approaches targeting these proteins have been minimal. Further research is required to fully understand the causes of the disease, how it progresses, which systems are affected and how it can be treated efficiently and effectively. With the advent of high throughput sequencing technologies such as transcription microarrays, methylation arrays and RNA sequencing, a wealth of high quality data is being generated allowing for the tracking of changes at the genetic level over the course of the disease. This information was analysed using machine learning methods including the in-house Stepwise Artificial Neural Network algorithm as well as the Network Inference algorithm developed by Graham Ball and his research group to elucidate new molecular markers and drivers of the disease and also to evaluate existing ones. The results were analysed using a non-parametric systems biology approach to determine the impact of these markers on the systems involved in the disease and new techniques including the driver analysis were developed to reduce bias and increase clarity. In order to achieve the most comprehensive set of results and reduce the risk of error and false discovery, the E-GEOD-48350 dataset was selected for its comprehensive and high-quality data and was used to test both old and new methods and obtain a preliminary set of results. These results were validated using other transcription datasets as well as an RNA sequencing dataset, leading to the identification of dysregulated genes related to microtubule stabilisation and immune system regulation in Alzheimer's disease, providing a foundation for further expansion and research.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available