Year: 2017
Type: dissertation
Abstract: Developing strategies to cope with increase in the ageing population and age-related chronic diseases is one of the societies biggest challenges.The characteristics of the ageing process shows significant inter-individual variation.Building genomic signatures that could account for variation in health outcomes with age may facilitate early prognosis of individual age-correlated diseases (e.g.cancer, coronary artery diseases and dementia) and help in developing better targeted treatments provided years in advance of acquiring disabling symptoms for these diseases.The aim of this thesis was to explore methods for diagnosing molecular features of human ageing.In particular, we utilise multi-platform transcriptomics, independent clinical data and classification methods to evaluate which human tissues demonstrate a reproducible molecular signature for age and which clinical phenotypes correlated with these new RNA biomarkers.Using machine-learning approach (kNN), applied to RNA data derived from muscle tissues from healthy donors, we developed a novel and statistically robust neuro-muscular 150 probe-set RNA signature and demonstrated its potential as a health-status diagnostic.Validated using
Author: Sanjana Sood
Language: English
Subfield: Aging
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2623960437