In-silico evaluation of aging-related interventions using omics data and predictive modeling

Area of Science:

• Aging research
• Computational biology
• Biomarker discovery

Background:

• Aging interventions face challenges in long-term clinical evaluation.
• In-silico methods using omics-based biomarkers are crucial for predicting intervention efficacy.
• Current predictive models often trained on observational data may yield biased results.

Purpose of the Study:

• To review and prioritize methodologies for evaluating aging interventions using computational approaches.
• To highlight the importance of using intervention data, safety assessments, and standardized benchmarks.
• To discuss various feature processing and predictive modeling techniques, including AI.

Main Methods:

• Review of existing evaluation methodologies for aging interventions.
• Prioritization of training predictive models on intervention data over observational data.
• Exploration of feature processing, linear/non-linear modeling, automated machine learning, and AI.

Main Results:

• The emergence of the first lifespan extension classifiers trained on intervention data.
• Emphasis on the necessity of safety and toxicity assessments in predictive modeling.
• Discussion of standardized benchmarks for reliable evaluation.

Conclusions:

• Explainable and reproducible strategies are essential for computational aging research.
• Integration of safety metrics and validation against interventional benchmarks are critical for accurate predictor development.
• Future directions emphasize robust validation of AI-driven aging intervention predictions.