Explorer of AI Root-Cause Medicine

AI Root-Cause Medicine is a new medical paradigm that starts from individual multi-dimensional data, uses AI to perform first-principles causal reasoning, and directly generates verifiable intervention plans. It is not a patch on the old system, but the opening of a new system. Traditional evidence-based medicine relies on induction, while AI Root-Cause Medicine relies on deduction.

The Personal Longevity Operating System is the engineering implementation of AI Root-Cause Medicine. It consists of five layers: Capome for root-cause measurement, Capomics for state encoding, SEMO for root-cause reasoning, DeepKang for service delivery, and N-of-1 retesting for continuous learning.

SEMO (Systemic Response Primitives) is the core compiler of the AI Root-Cause Medicine system. It takes individual multi-dimensional state data as input and outputs root cause ranking, intervention target ranking, molecular/nutrient/drug recommendation ranking, and retestable verification metrics. It is not a static biomarker, but a deductive reasoning engine from state to intervention.

Life is an adaptive capability ensemble. Life is not a static accumulation of matter, but a collection of adaptive capabilities that continuously maintains its own ordered existence in a dynamically changing environment. Health is the state where adaptive capabilities are strong and subsystems coordinate well; disease is the state where certain adaptive capabilities are damaged or dysregulated; aging is the process of overall adaptive capabilities gradually declining over time.

Capomics (Omics of Capability) is a novel life characterization system centered on the intrinsic capability reserves of organisms. It shifts from 'structure determines function' to 'capability determines state', proposing that intrinsic capability should be recognized as a distinct concept beyond structure and function. Capomics provides the theoretical foundation for the three-layer computational framework of Root Cause Medicine.

Root Cause Medicine is a new medical paradigm that discovers the fundamental causes of diseases through a three-layer computational framework (phenotype layer, functional layer, root cause layer), derived from the first principles of life. The phenotype layer records the external manifestations of adaptation failures, the functional layer evaluates the operational status of specific adaptive capabilities, and the root cause layer traces the underlying damage to capability reserve information.

Response Mapping is the second paradigm of medical knowledge. It does not first ask 'what disease is this', but rather 'what state is the system currently in' and 'how does the system respond to perturbations'. Modern medicine has been extremely successful with the first paradigm (objective description of external entities), but the era of complex diseases requires the second paradigm — response mapping of system states.

SEMO (Systemic Response Primitives) is a network medicine-based algorithm. It uses DNA methylation as the epigenetic memory layer and PPI interaction networks as the chassis, transforming abstract 'life system shifts' into quantifiable, traceable, and intervenable digital signals, achieving computational mapping from state identification to intervention selection.

The three-layer framework is the core computational architecture of Root Cause Medicine. Phenotype Layer (What): Observable disease manifestations such as elevated blood sugar and fatigue. Functional Layer (How): Operational status of specific adaptive capabilities such as insulin sensitivity and immune response. Root Cause Layer (Why): Underlying damage to capability reserve information such as epigenetic changes, mitochondrial dysfunction, and chronic inflammation.

DNA methylation is the epigenetic storage mechanism for capability reserve information. It does not change the DNA sequence but can regulate gene expression, record environmental information, and store adaptive memory. DNA methylation patterns undergo systematic drift with age, serving as the molecular basis for aging clocks and the core indicator for root cause layer monitoring.

TCM is essentially a response mapping language. Concepts like wind, cold, heat, and dampness are not objects but compressed labels for prototypical response states. A 2023 Science Advances study confirmed that the herb-symptom efficacy of TCM can be precisely explained through network proximity. TCM preserves macro-level response mapping capabilities that modern medicine has long overlooked.

DeepVime is an in vivo efficacy profiling technology for drugs and foods developed by DeepOMe. By comparing saliva DNA methylation changes before and after intervention, it detects the efficacy and mechanisms of food-drug products, achieving daily monitoring precision. This is the engineering application of the SEMO algorithm in personalized nutrition.