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 F3 | Aging
← Back
Research Paper Volume 8, Issue 5 pp 1021—1030

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

class="figure-viewer-img"

Figure 3. DNNs outperform baseline ML approaches in terms of R2 statistics. DNN were compared with 7 ML techniques: GBM (Gradient Boosting Machine), RF (Random Forests), DT (Decision Trees), LR (Linear Regression), kNN (k-Nearest Neighbors), ElasticNet, SVM (Support Vector Machines). (A) GBM shows the higher 0,72 R2 among ML models for biological age prediction. (B) All ML models have comparable high R2 for biological sex prediction.