Machine learning predicts lifespan and underlying causes of death in aging C. elegans
Kern, C.; Manescu, P.; Cuffaru, M.; Au, C.; Zhange, A.; Wang, H.; Gilliat, A. F.; Ezcurra, M.; Gems, D.
Show abstract
Senescence (aging) leads to senescent pathology that causes death, and genes control aging by determining such pathology. Here we investigate how senescent pathology mediates the effect of genotype on lifespan in C. elegans by means of a data-driven approach, using machine learning (ML). To achieve this we gathered extensive data on how diverse determinants of lifespan (sex, nutrition, genotype) affect patterns of age-related pathology. Our findings show that different life-extending treatments result in distinct patterns of suppression of senescent pathology. By analysing the differential effects on pathology and lifespan, our ML models were able to predict >70% of lifespan variation. Extent of pathology in the pharynx and intestine were the most important predictors of lifespan, arguing that elderly C. elegans die in part due to late-life disease in these organs. Notably, the mid-life pathogenetic burst characteristic of hermaphrodite senescence is absent from males.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Overexpression of Ssd1 and calorie restriction extend yeast replicative lifespan by preventing deleterious age-dependent iron uptake 96%
- The Neuron-specific IIS/FOXO Transcriptome in Aged Animals Reveals Regulatory Mechanisms of Neuronal and Cognitive Aging 96%
- fmo-4 promotes longevity and stress resistance via ER to mitochondria calcium regulation in C. elegans 96%
Similar papers in this journal
- Amyloid β accelerates age-related proteome-wide protein insolubility. 95%
- Antioxidants green tea extract and nordihydroguaiaretic acid confer species and strain specific lifespan and health effects in Caenorhabditis nematodes 95%
- Longevity interventions in Titan mice attenuate frailty and senescence accumulation 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.