Hillson, Olivia Valerie; (2023) An integrated machine learning and experimental approach to uncover ageing-associated processes in Fission Yeast. Doctoral thesis (Ph.D), UCL (University College London).

Preview

Text ohillson_thesis.pdf - Other Download (2MB) | Preview

Abstract

This work attempts to bring together knowledge of different pathways associated with cellular ageing and create connections between them using both machine learning and experimental methods. Initially, I describe the development of a novel proxy for chronological lifespan as part of the analysis pipeline of a high-throughput chronological lifespan assay in fission yeast. I then use this technique to go on to develop novel machine learning models that can predict lifespan, a complex phenotype, from simple traits, and identify ageing-associated phenotypes in fission yeast. Complementary to this, I investigate a transcription factor of interest, Hsr1, for its involvement in cellular ageing and ageing-associated processes. I describe direct regulatory targets and how it forms a network with at least four other ageing-associated transcription factors which bridges the gaps between models of ageing, and suggest mechanisms for these interactions. In this way, this work provides novel links between cellular ageing mechanisms and ageing-associated processes from both machine learning and experimental sources.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as XMLCSVJSON

Archive Staff Only

View Item