A review of supervised machine learning applied to ageing research

Fabris, Fabio, de Magalhes, Joao Pedro and Freitas, Alex A (2017) A review of supervised machine learning applied to ageing research. BIOGERONTOLOGY, 18 (2). pp. 171-188.

Text biogerontology17_machine_learning_aging.pdf - Published version Download (588kB)

Abstract

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Item Type:	Article
Uncontrolled Keywords:	Supervised machine learning, Ageing, Model interpretation
Depositing User:	Symplectic Admin
Date Deposited:	23 May 2018 09:12
Last Modified:	19 Jan 2023 06:33
DOI:	10.1007/s10522-017-9683-y
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3021646