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Figure 1. AI-empowered closed loop framework for longevity research.
This schematic outlines the integrated pipeline of Longevity Intelligence, a unified discovery framework that leverages evolutionary insights from non-traditional longevity model organisms and multi-omics data to uncover fundamental mechanisms of longevity. (A) Longevity Model Organisms: Core Traits - species with exceptional lifespan and resistance to age-related diseases (e.g., bowhead whale, naked mole-rat, certain testudines and deep-sea fish) serve as biological anchors for comparative analysis; (B) Longevity Organisms Multiomics Data Layer - high-resolution genomic, transcriptomic, epigenomic, proteomic, and metabolomic profiles are collected across species and life stages to build comprehensive datasets; (C) AI-Driven Computational Inference Engine - deep learning models, causal inference networks, and simulation tools integrate multi-omics data to predict anti-aging pathways, identify conserved molecular signatures, and generate testable hypotheses; (D) Discovery and Experimental Validation - predicted targets undergo high-throughput screening, cellular aging assays, cross-species functional validation, and in vivo testing, with closed-loop feedback refining computational models. This iterative cycle enables the translation of evolutionary insights into actionable strategies for extending healthspan. [This figure was created in BioRender. Shi, Z. (2026) https://BioRender.com/nuwpcd3.] AI: Artificial intelligence.