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DisMech and Individual/Case-Level Data

Overview

DisMech is a disease-level knowledge layer — it captures general mechanistic knowledge about disorders (pathophysiology, phenotypes, genetics, treatments, trajectories). It deliberately does not store individual patient records, cohort datasets, or per-patient variant calls.

The question is: how does this knowledge model inform interpretation of individual-level observations?

Existing schema touchpoints

The schema already has several hooks into case-level data, though they are not fully developed:

  • Phenopacket-store prefix — a phenopacket-store: CURIE prefix exists for linking to GA4GH Phenopacket collections.
  • PHENOPACKETS dataset type — datasets can be typed as case-level phenotype collections.
  • DIGITAL_TWIN computational model type — patient-specific computational models are recognized as a model category.
  • Frequency enums — phenotype frequencies (OBLIGATE through VERY_RARE) describe how often a phenotype appears across individuals with a given disorder.
  • Variants with clinical significance — ACMG-style pathogenic/benign classification connects to individual variant interpretation.

Use cases

1. Phenotype-driven differential diagnosis

A patient presents with a set of HPO-coded phenotypes. DisMech entries annotate phenotypes with frequencies, categories, and mechanism links. An interpretation system could:

  • Match patient phenotypes against DisMech phenotype lists.
  • Rank candidate disorders by the proportion and frequency of matching phenotypes.
  • Highlight diagnostic phenotypes (the diagnostic: true flag exists in the schema).
  • Use the pathophysiology graph to check whether the patient's phenotype constellation is mechanistically coherent (i.e., the observed phenotypes can all be traced to a common upstream mechanism).

2. Variant interpretation in mechanistic context

A patient has a VUS (variant of uncertain significance) in gene X. DisMech captures:

  • Which genes are involved in which diseases (the genetic section with GeneDescriptor).
  • How those genes participate in pathophysiology (pathway membership, downstream causal edges).
  • Known variants and their clinical significance.
  • Penetrance and expressivity estimates.

This lets you ask: "Does this patient's variant sit in a gene whose known mechanistic role explains their phenotype pattern?" — going beyond simple gene-disease association to mechanistic plausibility.

3. Treatment selection guided by pathophysiology subtype

DisMech links treatments to the pathophysiology mechanisms they target via TreatmentEffectEnum (INHIBITS, ACTIVATES, BYPASSES, RESTORES). For a patient whose subtype is known:

  • Match the patient's subtype to the relevant pathophysiology branch.
  • Identify treatments that target that specific mechanism.
  • Use clinical trial data (already modeled in DisMech) to assess evidence strength.
  • Flag treatments that target the wrong mechanism for the patient's subtype.

4. Comorbidity trajectory prediction

The schema has a full comorbidity/trajectory model (ComorbidityDirectionEnum, AssociationSignalSourceEnum, CausalEdge with sequelae). For an individual with disease A:

  • Query DisMech for directed comorbidity pairs where A precedes B.
  • Estimate risk based on population-level metrics (OR, HR, RR — all in AssociationMetricTypeEnum).
  • Prioritize surveillance for downstream conditions.
  • This is the "digital twin" use case — projecting a patient's disease trajectory from population-level mechanistic knowledge.

5. Phenopacket enrichment and annotation

A GA4GH Phenopacket describes an individual case. DisMech could enrich it by:

  • Annotating each phenotype with its mechanistic context (which pathophysiology node produces it).
  • Flagging phenotypes that are unexplained by the diagnosed disease's known mechanisms.
  • Suggesting missing phenotypes that should be screened for given the mechanisms involved.
  • Adding biological process and cell type context to raw HPO terms.

6. Case-cohort comparison

Compare an individual's phenotype profile against the "expected" profile from dismech:

  • Identify phenotypes the patient has that are VERY_RARE for their diagnosis (possible alternate or additional diagnosis).
  • Identify OBLIGATE phenotypes they lack (possible misdiagnosis).
  • Flag discordance between observed and expected severity or progression.

Architectural model

The cleanest integration pattern treats DisMech as an interpretive scaffold that sits alongside — not within — individual data systems:

flowchart TD
    A["Individual data<br/>(Phenopackets / OMOP / FHIR)"]
    B["Interpretation Engine<br/><em>match, score, explain</em>"]
    C["DisMech knowledge base<br/>(mechanisms, phenotypes,<br/>frequencies, treatments, trajectories)"]
    A --> B
    B --> C

DisMech already has the ontology bindings (HP, MONDO, CL, GO, MAXO, CHEBI) that make this interoperable — a Phenopacket's HPO terms directly match DisMech's PhenotypeDescriptor terms. The datasets section with PHENOPACKETS type already supports linking to case-level collections.

What DisMech is NOT

DisMech should not attempt to become an individual-data store. The boundary is:

Concern Belongs in DisMech Belongs elsewhere
Disease mechanisms Yes —
Phenotype frequencies Yes —
Treatment-mechanism links Yes —
Gene-disease associations Yes —
Individual patient records — Phenopackets, EHR
Cohort-level datasets — OMOP, FHIR
Per-patient variant calls — VCF, GA4GH VRS
Clinical decision support rules — CDS systems

The gap to fill is primarily an interpretation layer that queries DisMech programmatically and applies its knowledge to individual observations — not changes to DisMech's own data model.