Alex Freitas - Machine Learning for Ageing Research
|Starts:||13:00 10 Feb 2021|
|Ends:||14:00 10 Feb 2021|
|What is it:||Seminar|
|Organiser:||Department of Mathematics|
|Who is it for:||University staff, External researchers, Current University students|
Join us for this seminar by Alex Freitas (Kent) as part of the North West Seminar Series in Mathematical Biology and Data Sciences. Details of the full series can be found here https://www.cms.livjm.ac.uk/APMSeminar/
Please contact email@example.com or I.Siekmann@ljmu.ac.uk for the zoom link, or sign up to the mailing list.
Abstract: Traditionally, medicine focuses on combating one disease at a time. However, with the growing increase in the proportion of elderly people in the population of many countries, there has been an increasing interest on ageing research, since old age is one of the greatest risk factors for many diseases (e.g. most types of cancer). Hence, a greater understanding of the underlying ageing process could in principle lead to a better understanding of several age-related diseases and to a better health from a more “holistic” perspective. Due to the difficulty of doing biomedical experiments on ageing, particularly with humans, machine learning is being increasingly used for analyzing ageing-related data. This talk will briefly discuss some applications of machine learning algorithms to age-related data, focusing on supervised machine learning (mainly classification) methods and how they can be adapted to the specific characteristics of biomedical age-related datasets.
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Organisation: University of Kent
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