http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis." name="description"> Deep biomarkers of human aging: Application of deep neural networks to biomarker development - Figure F2 | Aging
Research Paper Volume 8, Issue 5 pp 1021—1030

Deep biomarkers of human aging: Application of deep neural networks to biomarker development

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Figure 2. Analysis of best DNN model in the ensemble and the whole ensemble. (A) Correlation between actual and predicted age values by the best DNN in the ensemble. (B) Biological age epsilon-prediction accuracy plot for the best DNN. (C) Biological age marker Importance, performed using FPI method. (D) Correlation between actual and predicted age values by whole ensemble based on ElasticNet model. (E) Biological age epsilon-prediction accuracy plot for the ensemble. (F) Heat map for Pearson's correlation coefficients between 40 DNNs. Scale bar colors indicate the sign and magnitude of Pearson's correlation coefficient between predictions of DNNs.