A review of supervised machine learning applied to ageing research

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.

Biphasic Modeling of Mitochondrial Metabolism Dysregulation during Aging

“…collect new data with rigorous and novel methods to ensure that the results obtained are relevant to the physiological process of aging. With the important introduction of machine learning approaches to handle large data sets to increase our understating of complex changes during aging [75], experimental artifacts may hinder correct and comprehensive conclusions. We expect that new models of eukaryotic aging will emerge using novel techniques and will therefore reassess the basic assumptions regarding metabolic dysfunction during aging.…”

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

“…Modern techniques allow such analyses to be taken further. Machine learning is increasingly being used in ageing research and offers a lot of potential for the identification of ageing and ageingrelated disease genes (Fabris, De Magalhães and Freitas, 2017). Machine learning methods complement traditional bioinformatics analyses, providing a different perspective and with the potential for more predictive results (Fabris et al, 2019).…”

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review

“…The results or "performance" of an AI algorithm depend on the model selected, available data, and the input features the researchers selected to predict an outcome. Below we narrate the most common classes of ML used for healthcare purposes: supervised and unsupervised machine learning (SL and UL) (Bzdok et al, 2018;Fabris et al, 2017;Miotto et al, 2016), and deep learning (DL) (Esteva et al, 2019;Miotto et al, 2017) (Fig. 1a), which may or may not involve natural language processing (NLP) (Demner-Fushman et al, 2009; Hirschberg and Manning, 2015) ( Fig.…”

“…Supervised Learning (SL) approaches require pre-labeled data (e.g., diagnosis of cognitive impairment vs. unimpaired) that serve as known outcomes for training an algorithm along with features derived from additional datastreams (e.g., clinical notes, neuroimaging) (Bzdok et al, 2018;Fabris et al, 2017). The algorithm then determines which features are most predictive of the pre-labeled outcome.…”