Track II 10-Billion-Cell Alliance: Subtrack 5. Longevity(J28010)

Goal

For healthy longevity, build high-quality datasets based on clinical and multi-omics big data; develop a Healthy Longevity Large Language Model (LLM) to identify targets associated with healthy longevity. Further, conduct cell- and model-based experiments for health interventions, as well as cohort intervention and population-level intervention evaluations, to form an iterative closed-loop of “research–clinical–application.”

Foundation Model

Based on the MoE (Mixture-of-Experts) architecture of the Genos foundation model, build a dedicated model for longevity genome interpretation to enable efficient and accurate feature extraction and pattern recognition.

Bioinformatics Analysis

Perform cross-omics integrative analyses including whole-genome, single-cell multi-omics, proteomics, metabolomics, lipidomics, antibody repertoire profiling, and metagenomics to explore omics signatures of healthy longevity.

Experimental Focus

Functional experiments and clinical follow-up studies on different intervention approaches, including NK cell infusion (adoptive transfer) and stem cell exosome-based interventions, among others.

Vertical Applications

1. Build an integrated AI platform covering “longevity genome interpretation – clinical testing – health intervention.”;
2. Develop new targets and novel methods for health interventions, accelerating the translation of research outcomes into industry.

Applications include

• 1.A platform for mining longevity-associated features
• 2.B2B/B2C systems for healthy longevity assessment
• 3.A recommendation engine for health intervention strategies

Affiliated Branches

• Guangdong–Hong Kong–Macao Branch (Shenzhen)
• Hangzhou Branch (Hangzhou)

(1) Preferred Background

Genomics, Aging Biology, Computational Biology, AI Systems, or Biostatistics.

(2) Core Technical Skills

MoE architectures; genomic feature extraction; health risk modeling; large-scale data interpretation.

(3) Modeling Competency

Experience with aging biomarkers, biological age modeling, or risk stratification systems.

(4) Preferred Qualifications

Interest in translating research into B2B/B2C AI platforms; exposure to preventive medicine datasets.

(5) Personal Traits

Long-term strategic thinker, interdisciplinary curiosity, and strong interest in healthspan extension.