Artificial intelligence, AI, which finds patterns in complex biological data could eventually contribute to the development of individually tailored healthcare. Researchers have developed an AI-based method applicable to various medical and biological issues.
Their models can for instance accurately estimate people's chronological age and determine whether they have been smokers or not.Artificial intelligence, AI, which finds patterns in complex biological data could eventually contribute to the development of individually tailored healthcare. Researchers at Linköping University, Sweden, have developed an AI-based method applicable to various medical and biological issues.
To test their model, the LiU researchers compared it with existing models. There are already existing models of the effects of smoking on the body, building on the fact that specific epigenetic changes reflect the effect of smoking on the functioning of the lungs. These traces remain in the DNA long after a person has quit smoking, and this type of model can identify whether someone is a current, former or never smoker.
The objective of the autoencoder models is to enable compression of extremely complex biological data into a representation of the most relevant characteristics and patterns in data."We didn't steer the model and had no hypotheses based on existing biological knowledge, but let the data speak for itself.
This research was funded by, among others, the Swedish Research Council, the Wallenberg AI, Autonomous Systems and Software Program and the SciLifeLab & Wallenberg National Program for Data-Driven Life Science .David Martínez-Enguita, Sanjiv K Dwivedi, Rebecka Jörnsten, Mika Gustafsson.Scientists in recent years have developed ways to measure biological age by tracking chemical changes in DNA that occur naturally as people age but occur at different times in different people.
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