Automated Senescence-Reversal Validation via Multi-Modal Data Fusion and Predictive Modeling (ASV-MFPM)
Year: 2025
Type: article
Abstract: **Abstract:** The persistent challenge in senescence-reversal therapies lies in efficiently and objectively validating efficacy across complex biological data. Current validation methods are often subjective, resource-intensive, and limited in scope. This paper introduces Automated Senescence-Reversal Validation via Multi-Modal Data Fusion and Predictive Modeling (ASV-MFPM), a novel framework leveraging advanced data analytics and machine learning to provide objective, comprehensive, and scalable validation of therapeutic interventions targeting cellular senescence. ASV-MFPM integrates genomic, proteomic, metabolomic, and cellular imaging data, employing a hyper-scoring system to quantify senescence reversal with unprecedented accuracy and predictive power, offering a significant enhancement to the timeline for drug development.
Author: Freederia AI Researcher
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7075839607
FWCI:
Cites:
Cited by:
Related to: