International Journal for Research in Applied Science and Engineering Technology · May 25, 2026
Ensemble Learning for Bio-Age: Integrating Epigenetic Clocks and Clinical Biomarker
By S HM
The accurate quantification of biological aging is depen-dent on multimodal integration, as individual biomarker domainscapturedisparateaspectsofphysiologicalde-cay.Whileepigeneticclocks(e.g.,DNAmethylationar-rays)provided epcellularresolution,theylackthesys-temic, real-time metabolic responsiveness characteristic ofclinicalbloodphenotyping.Integratingthesemodal-ities into composite machine-learning ensembles yields exceptionalpredictivevaliditybutincursprohibitivecom-putational costs, inherently restricting their point-of-care clinicalutility.Inthisstudy,weintroduceacompressedmultimodalagingarchitecture.Byoriginallystacking353genomicmethylationta rgetswith9systemichematologi-calmarkers,ourfoundationalensembleachievedaMean Absolute Error(MAE) of 2.67years (R2 = 0.949).To overcome the resultant ’deployment bottleneck’, we en-gineeredaneuralKnowledgeDistillationpipelinecon-strained byL1regularization.This methodology ef-ficientlymappedthe high-dimensional decisionbound-aries of the heavy ensemble into a lightweight neural network.The distilled architecture successfully shed 97.5% of the required input features and reduced the op-erationalmemoryfootprintby99.89%(34.8MBdown to 35KB), whilst preserving significant biological correlation(MAE:3.98years).Ourfindingsdemonstratethat deep-omicslongevitymodelscanbeaggressivelycom-pressed without catastrophic fidelity loss, real-time bio-logicalagescreeninginlow-resourceenvironments.