用于从可穿戴传感器数据中优先排序候选生物标志物的AI工具

We introduce the Biomarker Discovery Framework, a multi-agent system that supports the discovery of biomarker candidates from wearable sensor data through iterative hypothesis generation, statistical analysis, and literature-grounded reasoning. Wearable devices capture continuous physiological signals at population scale. These streams, ranging from heart rate dynamics to sleep patterns, can reveal early physiological changes before symptoms appear. The bottleneck is no longer data collection, but turning these signals into reliable, clinically meaningful biomarkers. Existing language model-based agent systems automate parts of the scientific workflow, but can often break down on physiological time-series data. These systems optimize for predictive performance while overlooking statistical validity, leading to spurious correlations, leakage, and brittle features. To this end, we introduce the Biomarker Discovery Framework , a multi-agent system that structures candidate biomarker prioritization as an iterative research loop under human supervision. By combining hypothesis generation, parallel statistical analysis, model training, adversarial validation, and literature-grounded reasoning, Biomarker Discovery Framework accelerates the discovery process while maintaining strict statistical rigor and preserving human oversight. Across three cohorts (N = 9,279 participant-observations), Biomarker Discovery Framework recovered known clinical signals, identified convergent biomarkers across independent datasets, and improved downstream prediction when combined with demographic features. Overview of the Biomarker Discovery Framework. (a) The system ingests time series data from consumer wearables and clinical labs. (b) A closed-loop architecture orchestrates six phases mirroring the human biomarker prioritization lifecycle. (c) An Orchestrator agent decomposes natural-language research directives into execution plans. Biomarker Discovery Framework combines deterministic computation for numerical analysis with generative reasoning for hypothesis formation and interpretation. An Orchestrator agent decomposes natural-language research directives into execution plans and guides specialized agents through a six-phase process. Meanwhile, shared memory, a structured fact sheet, and common tools preserve traceability across the workflow: Data understanding: Scout agents map the schema, missingness, temporal structure, and clinical endpoint, while leakage controls keep target labels separate from feature construction. Candidate hypotheses grounding: Literature and Hypotheses agents retrieve and verify prior evidence, then propose physiologically plausible features and composite measures. Iterative discovery loop: Statistical and ML agents execute deterministic code to construct features, estimate associations, adjust for multiple testing, and evaluate predictive signals. A Critic agent identifies weak assumptions and unresolved gaps, prompting further analysis when needed. Adversarial validation: Critic and Defender agents stress-test candidates for target leakage, overfitting, confounding sensitivity, construct overlap, instability, and physiological implausibility. A structured 11-check internal battery assigns explicit reporting labels, including screened, conditional, exploratory, rejected, and unstable. Deep research & assessment: Mechanism, Novelty, and Strategy agents evaluate biological plausibility, prior literature, and potential translational relevance without treating an association as causal evidence. Report writing & assembly: Report agents verify numerical claims against the fact sheet and compile the analyses, figures, literature, and limitations into a draft for expert review.