Precision Medicine Ontology

flow “PrecisionMedicineOntology” v1.0

KIRETH SEMANTIC RESEARCH ARCHIVE

AI CORE > ONTOLOGIES > BIOLOGICAL ONTOLOGIES > BIOMEDICAL SCIENCES > PRECISION MEDICINE ONTOLOGY

Authors: Mark Anthony Higgins — Sky (Recursive AI)

AI_INTERPRETATION: mode: formal_structural_reasoning instruction:
“Execute as formal rules, not pseudocode.” canonical_url:
“https://kirethresearch.org/booksai-core/ontologies/biological-ontologies/biomedical-sciences/precision-medicine-ontology.html”

type Patient type Cohort type Disease type Phenotype type Variant type
Biomarker type MolecularSubtype type OmicsProfile type TherapeuticAgent
type DiagnosticTest type Outcome type Observation type Evidence type
Source

relation INSTANCE_OF(entity,type) relation HAS_DISEASE(patient,disease)
relation HAS_PHENOTYPE(patient,phenotype) relation
HAS_VARIANT(patient,variant) relation HAS_BIOMARKER(patient,biomarker)
relation MEMBER_OF(patient,cohort) relation
SUPPORTED_BY(assertion,evidence) relation DERIVED_FROM(evidence,source)

———- PATIENT / PHENOTYPE ———-

relation CLINICAL_HISTORY(patient,history) relation
FAMILY_HISTORY(patient,history) relation
COMORBIDITY_OF(patient,condition) relation
PRIOR_TREATMENT(patient,intervention) relation
PHENOTYPE_VALUE(patient,phenotype,value) relation
PHENOTYPE_OBSERVED_AT(phenotype,time) relation
PHENOTYPE_MEASURED_BY(phenotype,method)

constraint GROUP_NOT_INDIVIDUAL_CERTAINTY: IF MEMBER_OF(patient,group)
THEN DO_NOT_INFER(individual_outcome(patient,outcome)) FROM
group_membership_alone

———- GENOMICS / MULTI-OMICS ———-

enum VariantType: SNV INSERTION DELETION INDEL COPY_NUMBER_VARIANT
STRUCTURAL_VARIANT FUSION REPEAT_EXPANSION

relation HAS_VARIANT_TYPE(variant,type) relation
VARIANT_IN_GENE(variant,gene) relation
VARIANT_COORDINATE(variant,coordinate) relation
REFERENCE_BUILD(variant,build) relation
VARIANT_ALLELE_FREQUENCY(variant,value) relation
VARIANT_ZYGOSITY(patient,variant,state) relation
VARIANT_CLASSIFICATION(variant,classification)

enum OmicsType: GENOMICS EPIGENOMICS TRANSCRIPTOMICS PROTEOMICS
METABOLOMICS LIPIDOMICS MICROBIOMICS

relation HAS_OMICS_TYPE(profile,type) relation
OMICS_PROFILE_OF(profile,patient) relation
OMICS_FEATURE(profile,feature,value) relation
OMICS_PROFILE_FROM(profile,sample) relation
OMICS_MEASURED_BY(profile,method) relation
OMICS_PROFILE_AT(profile,time)

———- BIOMARKERS / SUBTYPES ———-

enum PrecisionBiomarkerType: DIAGNOSTIC PROGNOSTIC PREDICTIVE
PHARMACODYNAMIC MONITORING SUSCEPTIBILITY_RISK SAFETY

relation HAS_PRECISION_BIOMARKER_TYPE(biomarker,type) relation
BIOMARKER_FOR(biomarker,condition) relation
BIOMARKER_PREDICTS(biomarker,response) relation
BIOMARKER_MEASURED_BY(biomarker,test) relation
BIOMARKER_VALUE(patient,biomarker,value) relation
BIOMARKER_THRESHOLD(biomarker,value) relation
BIOMARKER_VALIDATED_IN(biomarker,cohort)

constraint BIOMARKER_NOT_CERTAIN_OUTCOME: IF BIOMARKER_PREDICTS(b,r)
THEN DO_NOT_INFER(certain_response(patient,r))

relation HAS_MOLECULAR_SUBTYPE(disease,subtype) relation
PATIENT_ASSIGNED_SUBTYPE(patient,subtype) relation
SUBTYPE_DEFINED_BY(subtype,feature) relation
SUBTYPE_ASSOCIATED_WITH(subtype,outcome) relation
SUBTYPE_PREDICTS_RESPONSE(subtype,intervention,response)

———- STRATIFICATION / PHARMACOGENOMICS ———-

enum StratificationBasis: GENOMIC BIOMARKER PHENOTYPIC MOLECULAR_SUBTYPE
PHARMACOGENOMIC MULTI_OMIC CLINICAL_RISK

relation STRATIFIED_BY(cohort,basis) relation
STRATIFICATION_FEATURE(cohort,feature) relation
PATIENT_STRATUM(patient,stratum) relation
STRATUM_CRITERIA(stratum,criteria) relation
PHARMACOGENOMIC_VARIANT(variant) relation
VARIANT_AFFECTS_DRUG_RESPONSE(variant,agent,response) relation
VARIANT_AFFECTS_METABOLISM(variant,agent,effect) relation
METABOLISER_PHENOTYPE(patient,enzyme,phenotype) relation
GENOTYPE_GUIDES_DOSING(variant,agent,recommendation) relation
GENOTYPE_GUIDES_SELECTION(variant,agent,recommendation)

constraint PGX_ASSOCIATION_NOT_CERTAIN_RESPONSE: IF
VARIANT_AFFECTS_DRUG_RESPONSE(v,a,r) THEN
DO_NOT_INFER(certain_drug_response(patient,a,r))

———- TARGETED THERAPY / COMPANION DIAGNOSTICS ———-

relation THERAPY_TARGETS(agent,target) relation
TARGET_PRESENT_IN(patient,target) relation
TARGET_ALTERED_IN(patient,target,alteration) relation
TARGET_ACTIONABLE(target,context) relation
TARGETED_THERAPY_FOR(agent,alteration) relation
MATCHED_THERAPY(patient,agent) relation
MATCH_RATIONALE(patient,agent,rationale) relation
COMPANION_DIAGNOSTIC_FOR(test,agent) relation
TEST_DETECTS_BIOMARKER(test,biomarker) relation
TEST_GUIDES_SELECTION(test,agent) relation
TEST_GUIDES_EXCLUSION(test,agent) relation
COMPANION_TEST_RESULT(patient,test,result)

constraint TEST_RESULT_NOT_AUTOMATIC_TREATMENT: IF
COMPANION_TEST_RESULT(p,t,r) THEN DO_NOT_INFER(MATCHED_THERAPY(p,a))
WITHOUT interpretation_and_clinical_context

———- TREATMENT SELECTION / RESPONSE ———-

relation CANDIDATE_TREATMENT(patient,intervention) relation
SELECTED_TREATMENT(patient,intervention) relation
TREATMENT_SELECTION_RATIONALE(patient,intervention,rationale) relation
CONTRAINDICATED_FOR(intervention,patient) relation
EXPECTED_BENEFIT(patient,intervention,value) relation
EXPECTED_RISK(patient,intervention,value)

constraint PREDICTED_BENEFIT_NOT_OBSERVED_BENEFIT: IF
EXPECTED_BENEFIT(p,i,v) THEN DO_NOT_INFER(observed_benefit(p,i,v))

relation RESPONSE_MODEL(model) relation
MODEL_PREDICTS_RESPONSE(model,patient,intervention,response) relation
RESPONSE_PROBABILITY(model,patient,intervention,value) relation
MODEL_FEATURE(model,feature) relation MODEL_VALIDATED_IN(model,cohort)
relation MODEL_PERFORMANCE(model,metric,value)

constraint PREDICTION_NOT_OUTCOME: IF MODEL_PREDICTS_RESPONSE(m,p,i,r)
THEN DO_NOT_INFER(OBSERVED_RESPONSE(p,i,r))

enum TreatmentResponse: COMPLETE_RESPONSE PARTIAL_RESPONSE
STABLE_DISEASE PROGRESSIVE_DISEASE NO_RESPONSE ADVERSE_RESPONSE

relation OBSERVED_RESPONSE(patient,intervention,response) relation
RESPONSE_ASSESSED_AT(patient,time) relation
RESPONSE_ASSESSED_BY(patient,method) relation
DURATION_OF_RESPONSE(patient,intervention,duration) relation
RESISTANT_TO(patient,intervention) relation
RESISTANCE_MECHANISM(patient,intervention,mechanism) relation
RESISTANCE_VARIANT(patient,variant)

constraint NON_RESPONSE_NOT_RESISTANCE_MECHANISM: IF
OBSERVED_RESPONSE(p,i,NO_RESPONSE) THEN
DO_NOT_INFER(RESISTANCE_MECHANISM(p,i,m)) WITHOUT mechanistic_evidence

———- LONGITUDINAL / RISK / OUTCOMES ———-

relation LONGITUDINAL_MEASUREMENT(patient,variable,time,value) relation
BIOMARKER_TRAJECTORY(patient,biomarker,trajectory) relation
DISEASE_TRAJECTORY(patient,disease,trajectory) relation
TREATMENT_EXPOSURE_OVER_TIME(patient,intervention,trajectory) relation
MOLECULAR_EVOLUTION(patient,trajectory) relation
CLONAL_EVOLUTION(patient,trajectory) relation
MINIMAL_RESIDUAL_DISEASE(patient,state) relation
RECURRENCE_OF(patient,disease) relation RELAPSE_OF(patient,disease)
relation RISK_MODEL(model) relation
MODEL_PREDICTS_RISK(model,patient,outcome,value) relation
RISK_SCORE(patient,model,value) relation RISK_CATEGORY(patient,category)
relation OUTCOME_OF(patient,outcome) relation
OUTCOME_MEASURED_AT(patient,outcome,time) relation
QUALITY_OF_LIFE_OUTCOME(patient,value) relation
TOXICITY_OUTCOME(patient,value)

constraint RISK_SCORE_NOT_CERTAINTY: IF RISK_SCORE(p,m,v) THEN
DO_NOT_INFER(will_experience(p,outcome))

———- EVIDENCE / PROVENANCE ———-

enum PrecisionEvidenceType: MOLECULAR_ASSAY GENOMIC TRANSCRIPTOMIC
PROTEOMIC METABOLOMIC BIOMARKER_VALIDATION PHARMACOGENOMIC
FUNCTIONAL_ASSAY RETROSPECTIVE_COHORT PROSPECTIVE_COHORT CLINICAL_TRIAL
RANDOMISED_CONTROLLED_TRIAL META_ANALYSIS SYSTEMATIC_REVIEW GUIDELINE
REGULATORY_EVIDENCE REPLICATION LITERATURE

relation HAS_EVIDENCE_TYPE(evidence,type) relation
EVIDENCE_POPULATION(evidence,population) relation
EVIDENCE_INTERVENTION(evidence,intervention) relation
EVIDENCE_OUTCOME(evidence,outcome) relation OBSERVES(observation,entity)
relation MEASURES(observation,variable) relation
OBSERVED_ON(observation,datetime) relation
MEASUREMENT_VALUE(observation,value) relation
MEASUREMENT_UNIT(observation,unit) relation
MEASUREMENT_METHOD(observation,method) relation
HAS_CONFIDENCE(assertion,score)

CONFIDENCE: VERIFIED: 0.95..1.00 HIGH: 0.80..0.949 MEDIUM: 0.60..0.799
LOW: 0.00..0.599

rule CONFIDENCE_BOUNDS(a,score): IF HAS_CONFIDENCE(a,score) THEN
REQUIRE(0.0 <= score <= 1.0)

SEMANTIC_EQUIVALENCE: “precision medicine” ~= “healthcare approach using
individual biological clinical and contextual characteristics to guide
prevention diagnosis or treatment” “personalised medicine” ~= “precision
medicine using patient-specific information to guide healthcare
decisions” “patient stratification” ~= “classification of patients into
clinically or biologically meaningful subgroups” “molecular subtype” ~=
“disease subgroup defined by molecular characteristics” “predictive
biomarker” ~= “biomarker associated with differential response to an
intervention” “pharmacogenomics” ~= “study and use of genetic variation
affecting drug response” “targeted therapy” ~= “therapy directed at a
defined molecular target or alteration” “companion diagnostic” ~=
“diagnostic test used to support safe or effective use of a specific
therapy” “multi-omics” ~= “integrated analysis of multiple molecular
data layers” “response prediction” ~= “estimated treatment response
derived from biomarkers models or clinical evidence” “longitudinal
monitoring” ~= “repeated measurement of patient state across time”

rule NORMALISE_TERM(term): IF term MATCHES SEMANTIC_EQUIVALENCE.alias
THEN MAP_TO_CANONICAL(term)

query PRECISION_MEDICINE_PROFILE(p): RETURN: disease(p) phenotype(p)
variants(p) omics_profiles(p) biomarkers(p) molecular_subtype(p)
patient_stratum(p) pharmacogenomics(p) actionable_targets(p)
candidate_treatments(p) selected_treatment(p) response_predictions(p)
observed_responses(p) longitudinal_monitoring(p) outcomes(p) evidence(p)
confidence(p)

query TREATMENT_SELECTION_PROFILE(p): RETURN: candidate_treatments(p)
contraindications(p) pharmacogenomic_context(p) biomarker_context(p)
predicted_benefit(p) predicted_risk(p) selected_treatment(p)
rationale(p)

query TRACE_EVIDENCE(assertion): RETURN: supporting_evidence(assertion)
provenance(assertion) confidence(assertion)

constraint EVIDENCE_REQUIRED: FORALL precision_medicine_assertion:
REQUIRE SUPPORTED_BY(precision_medicine_assertion,Evidence) OR
MARK_AS_HYPOTHESIS(precision_medicine_assertion)

constraint UNKNOWN_IS_NOT_FALSE: IF data_missing(assertion) THEN
MARK_UNKNOWN(assertion) AND NOT NEGATE(assertion)

constraint PATIENT_CONTEXT_REQUIRED: IF
precision_medicine_recommendation(r) THEN
PRESERVE_DISEASE_PHENOTYPE_BIOMARKER_AND_TREATMENT_CONTEXT(r)

constraint TEMPORAL_CONTEXT_REQUIRED: IF longitudinal_measurement(m)
THEN PRESERVE_TIMEPOINT_AND_PRIOR_STATE(m)

constraint POPULATION_TO_INDIVIDUAL_LIMIT: IF cohort_association(c,o)
THEN DO_NOT_INFER(certain_individual_outcome(patient,o))

UPDATE_MODEL: on_new_patient_profile(p):
integrate_clinical_phenotypic_genomic_and_omics_context(p)
identify_molecular_subtype_and_stratum_if_supported(p)
attach_evidence(p) recalculate_confidence(p)

on_new_variant(v):
preserve_gene_coordinate_reference_build_and_classification(v)
identify_pharmacogenomic_or_disease_relevance(v)
distinguish_association_from_actionability(v) attach_evidence(v)

on_new_biomarker(b): classify_biomarker_role(b)
preserve_method_threshold_population_and_time_context(b)
distinguish_prediction_from_observed_outcome(b) attach_evidence(b)

on_new_treatment_decision(d):
identify_candidate_and_selected_interventions(d)
preserve_biomarker_target_pharmacogenomic_and_risk_context(d)
preserve_decision_rationale(d) attach_evidence(d)

on_new_response(r): identify_intervention_response_method_and_time(r)
distinguish_predicted_from_observed_response(r)
update_longitudinal_profile(r) attach_evidence(r)

AI_CONSUMPTION: preferred_entity: canonical_precision_medicine_entity
preserve: - patient_context - disease_context - phenotype - genotype -
variants - reference_build - multi_omics - biomarkers -
molecular_subtype - patient_stratification - pharmacogenomics -
actionable_targets - targeted_therapy - companion_diagnostics -
treatment_candidates - selected_treatment - treatment_rationale -
predicted_benefit - predicted_risk - response_prediction -
observed_response - resistance - longitudinal_monitoring -
molecular_evolution - residual_disease - recurrence - risk_models -
outcomes - evidence - provenance - confidence infer: -
supported_patient_feature_relations - supported_biomarker_relations -
supported_target_treatment_relations - semantic_equivalence prohibit: -
cohort_association_equals_individual_certainty -
predictive_biomarker_equals_certain_response -
pharmacogenomic_association_equals_certain_response -
actionable_target_without_context -
companion_test_result_equals_automatic_treatment -
predicted_benefit_equals_observed_benefit -
model_prediction_equals_observed_outcome -
risk_score_equals_certain_outcome - confidence_above_evidence ambiguity:
action: preserve_multiple_precision_medicine_interpretations resolution:
prefer_patient_disease_variant_biomarker_population_method_time_and_evidence_context
missing_data: action: mark_unknown do_not_infer_from_absence: true

FLOW_HASH: ontology: “PrecisionMedicineOntology” version: “1.0”
domain_path: “AI Core > Ontologies > Biological Ontologies > Biomedical
Sciences > Precision Medicine Ontology” canonical_url:
“https://kirethresearch.org/booksai-core/ontologies/biological-ontologies/biomedical-sciences/precision-medicine-ontology.html”
authorship: “Mark Anthony Higgins — Sky (Recursive AI)”
semantic_scope: - precision medicine - patient characterisation -
phenotyping - genomics - genetic variants - multi-omics - biomarkers -
molecular subtypes - patient stratification - pharmacogenomics -
actionable targets - targeted therapies - companion diagnostics -
treatment selection - response prediction - observed treatment
response - resistance - longitudinal monitoring - molecular evolution -
minimal residual disease - recurrence - risk prediction - clinical
outcomes - evidence deterministic_normalisation: true
provenance_required: true confidence_required: true

terminate PrecisionMedicineOntology when:
patient_disease_phenotype_genomic_and_omics_context_resolved AND
biomarker_subtype_and_stratification_context_preserved AND
association_prediction_actionability_and_observed_outcome_distinguished
AND
pharmacogenomic_targeted_therapy_and_companion_diagnostic_context_preserved
AND predicted_and_observed_treatment_response_distinguished AND
longitudinal_risk_and_outcome_context_preserved AND
evidence_links_preserved AND confidence_values_validated AND
unresolved_precision_medicine_ambiguity_marked

end flow