Biomedical Science Ontology

flow “BiomedicalScienceOntology” v1.0

KIRETH SEMANTIC RESEARCH ARCHIVE

AI CORE > ONTOLOGIES > BIOLOGICAL ONTOLOGIES > BIOMEDICAL SCIENCES > BIOMEDICAL SCIENCE 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/biomedical-science-ontology.html”

type BiologicalEntity type Molecule type Gene type Protein type Cell
type Tissue type Organ type OrganSystem type PhysiologicalProcess type
PathologicalProcess type Disease type Biomarker type DiagnosticTest type
TherapeuticAgent type Intervention type ExperimentalModel type
ClinicalSample type Observation type Evidence type Source

relation INSTANCE_OF(entity,type) relation PART_OF(part,whole) relation
HAS_PART(whole,part) relation LOCATED_IN(entity,location) relation
PARTICIPATES_IN(entity,process) relation INTERACTS_WITH(a,b) relation
REGULATES(regulator,target)

———- MOLECULAR / CELLULAR ———-

relation GENE_ENCODES(gene,protein) relation EXPRESSES(cell,gene)
relation TRANSCRIBES(cell,gene,rna) relation
TRANSLATES(cell,rna,protein) relation PROTEIN_PERFORMS(protein,function)
relation MOLECULE_BINDS_TO(a,b) relation MOLECULE_ACTIVATES(a,target)
relation MOLECULE_INHIBITS(a,target) relation CELL_OF(cell,tissue)
relation HAS_CELL_TYPE(cell,type) relation
DIFFERENTIATES_INTO(cell,target) relation
CELL_SIGNAL_RECEIVES(cell,signal) relation CELL_PROLIFERATES(cell,event)
relation CELL_DIES_BY(cell,process)

———- PHYSIOLOGY / PATHOLOGY ———-

relation TISSUE_OF(tissue,organ) relation ORGAN_OF(organ,system)
relation TISSUE_FUNCTION(tissue,function) relation
ORGAN_FUNCTION(organ,function) relation
ORGAN_SYSTEM_FUNCTION(system,function) relation
PHYSIOLOGICAL_VALUE(variable,value) relation
HAS_REFERENCE_RANGE(variable,range) relation
HOMEOSTATIC_PROCESS(process) relation
HAS_PATHOLOGICAL_PROCESS(disease,process) relation
PATHOLOGICAL_PROCESS_AFFECTS(process,entity) relation
DISEASE_AFFECTS(disease,organ) relation
DISEASE_AFFECTS_SYSTEM(disease,system) relation
HAS_PATHOPHYSIOLOGY(disease,process) relation
HAS_SEVERITY(disease,value) relation CAUSED_BY(disease,cause) relation
CONTRIBUTED_TO_BY(disease,factor) relation
ASSOCIATED_WITH(disease,factor) relation RISK_FACTOR_FOR(factor,disease)

constraint ASSOCIATION_NOT_CAUSATION: IF ASSOCIATED_WITH(d,f) THEN
DO_NOT_INFER(CAUSED_BY(d,f)) WITHOUT causal_evidence

———- IMMUNOLOGY / INFECTION ———-

relation IMMUNE_COMPONENT_RECOGNISES(component,target) relation
ANTIGEN_PRESENTED_BY(antigen,cell) relation
CYTOKINE_SIGNALS_TO(cytokine,cell) relation
ANTIBODY_BINDS_TO(antibody,antigen) relation
INFLAMMATORY_RESPONSE_TO(system,stimulus) relation
IMMUNE_MEMORY_TO(system,antigen) relation
PATHOGEN_INFECTS(pathogen,host) relation
PATHOGEN_INVADES(pathogen,tissue) relation
PATHOGEN_REPLICATES_IN(pathogen,cell) relation
HOST_RESPONSE_TO(host,pathogen,response) relation
TRANSMISSION_ROUTE(pathogen,route) relation
VIRULENCE_FACTOR_OF(factor,pathogen)

———- GENETICS / OMICS ———-

relation HAS_VARIANT(entity,variant) relation
VARIANT_LOCATED_IN(variant,gene) relation
VARIANT_ASSOCIATED_WITH(variant,trait) relation
VARIANT_AFFECTS(variant,function) relation
GENETIC_RISK_FOR(variant,disease) relation
GENE_EXPRESSION_LEVEL(sample,gene,value) relation
HAS_EPIGENETIC_MARK(region,mark) relation
DNA_METHYLATION_LEVEL(region,value) relation
TRANSCRIPT_ABUNDANCE(sample,transcript,value) relation
PROTEIN_ABUNDANCE(sample,protein,value) relation
METABOLITE_ABUNDANCE(sample,metabolite,value)

constraint GENETIC_ASSOCIATION_NOT_DETERMINISM: IF GENETIC_RISK_FOR(v,d)
THEN DO_NOT_INFER(will_develop(entity,d))

———- BIOMARKERS / DIAGNOSTICS ———-

enum BiomarkerType: DIAGNOSTIC PROGNOSTIC PREDICTIVE PHARMACODYNAMIC
SAFETY MONITORING SUSCEPTIBILITY_RISK

relation HAS_BIOMARKER_TYPE(biomarker,type) relation
BIOMARKER_FOR(biomarker,condition) relation
BIOMARKER_MEASURED_IN(biomarker,sample) relation
BIOMARKER_VALUE(biomarker,value) relation BIOMARKER_UNIT(biomarker,unit)
relation BIOMARKER_THRESHOLD(biomarker,value) relation
BIOMARKER_VALIDATED_IN(biomarker,population)

constraint BIOMARKER_NOT_DIAGNOSIS: IF BIOMARKER_VALUE(b,v) THEN
DO_NOT_INFER(HAS_DISEASE(subject,d)) WITHOUT diagnostic_context

relation DIAGNOSTIC_TEST_FOR(test,disease) relation
TEST_USES_SAMPLE(test,sample) relation TEST_MEASURES(test,analyte)
relation TEST_RESULT(test,result) relation TEST_VALUE(test,value)
relation TEST_UNIT(test,unit) relation SENSITIVITY(test,value) relation
SPECIFICITY(test,value) relation POSITIVE_PREDICTIVE_VALUE(test,value)
relation NEGATIVE_PREDICTIVE_VALUE(test,value)

———- PHARMACOLOGY / THERAPEUTICS ———-

relation THERAPEUTIC_AGENT(agent) relation AGENT_TARGETS(agent,target)
relation AGENT_BINDS_TO(agent,target) relation
AGENT_INHIBITS(agent,target) relation AGENT_ACTIVATES(agent,target)
relation HAS_MECHANISM_OF_ACTION(agent,mechanism) relation
DOSE_OF(agent,value) relation ABSORPTION_OF(agent,rate) relation
DISTRIBUTION_OF(agent,volume) relation METABOLISM_OF(agent,pathway)
relation EXCRETION_OF(agent,route) relation HALF_LIFE_OF(agent,duration)
relation CLEARANCE_OF(agent,value) relation
BIOAVAILABILITY_OF(agent,value) relation
DOSE_RESPONSE(agent,response,curve) relation
THERAPEUTIC_EFFECT(agent,effect) relation ADVERSE_EFFECT(agent,effect)
relation THERAPEUTIC_WINDOW(agent,range)

enum InterventionType: PHARMACOLOGICAL BIOLOGICAL GENE_BASED CELL_BASED
SURGICAL DEVICE_BASED RADIATION REHABILITATIVE PREVENTIVE

relation HAS_INTERVENTION_TYPE(intervention,type) relation
TARGETS(intervention,disease) relation RECEIVES(subject,intervention)
relation RESPONSE_TO(subject,intervention,response) relation
OUTCOME_OF(intervention,outcome) relation
ADVERSE_EVENT_OF(event,intervention)

———- MODELS / SAMPLES / TRANSLATION ———-

enum ExperimentalModelType: CELL_CULTURE PRIMARY_CELL ORGANOID
ORGAN_ON_CHIP ANIMAL_MODEL COMPUTATIONAL_MODEL EX_VIVO_TISSUE
HUMAN_COHORT

relation HAS_MODEL_TYPE(model,type) relation
MODEL_OF(model,disease_or_process) relation
MODEL_RECAPITULATES(model,feature) relation
MODEL_LIMITATION(model,limitation)

constraint MODEL_NOT_IDENTICAL_TO_HUMAN: IF MODEL_OF(m,d) THEN
DO_NOT_INFER(model_equals_human_disease(m,d))

relation SAMPLE_FROM(sample,subject) relation
SAMPLE_COLLECTED_AT(sample,time) relation
SAMPLE_PROCESSED_BY(sample,method) relation
SAMPLE_STORED_AT(sample,condition) relation
PRECLINICAL_EVIDENCE_FOR(evidence,intervention) relation
CLINICAL_EVIDENCE_FOR(evidence,intervention) relation
TRANSLATES_FINDING_TO(finding,application) relation
VALIDATED_IN(finding,cohort) relation REPLICATED_IN(finding,study)

constraint PRECLINICAL_NOT_CLINICAL_EFFECT: IF
PRECLINICAL_EVIDENCE_FOR(e,i) THEN DO_NOT_INFER(clinically_effective(i))
WITHOUT clinical_evidence

———- STUDIES / STATISTICS ———-

relation STUDY_POPULATION(study,population) relation
STUDY_INTERVENTION(study,intervention) relation
STUDY_COMPARATOR(study,comparator) relation STUDY_OUTCOME(study,outcome)
relation EFFECT_SIZE(study,value) relation
CONFIDENCE_INTERVAL(study,lower,upper) relation P_VALUE(study,value)
relation SAMPLE_SIZE(study,value)

constraint STATISTICAL_SIGNIFICANCE_NOT_IMPORTANCE: IF P_VALUE(s,p) THEN
DO_NOT_INFER(clinically_or_biologically_important(s))

———- EVIDENCE ———-

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
SUPPORTED_BY(assertion,evidence) relation DERIVED_FROM(evidence,source)
relation HAS_CONFIDENCE(assertion,score)

enum EvidenceType: MOLECULAR_ASSAY CELLULAR_ASSAY HISTOLOGY GENETIC
GENOMIC TRANSCRIPTOMIC PROTEOMIC METABOLOMIC MICROBIOLOGICAL
IMMUNOLOGICAL IMAGING PHYSIOLOGICAL PRECLINICAL ANIMAL_STUDY
ORGANOID_STUDY CLINICAL_OBSERVATION CLINICAL_TRIAL DIAGNOSTIC_STUDY
SYSTEMATIC_REVIEW META_ANALYSIS REPLICATION LITERATURE

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: “biomedical science” ~= “scientific investigation
of biological mechanisms relevant to health disease diagnosis and
treatment” “pathophysiology” ~= “functional biological changes
associated with disease” “pathology” ~= “study of disease mechanisms and
structural or functional changes” “biomarker” ~= “measurable biological
indicator” “diagnostic biomarker” ~= “biomarker used to aid diagnosis”
“prognostic biomarker” ~= “biomarker associated with future outcome”
“predictive biomarker” ~= “biomarker associated with differential
response to intervention” “pharmacokinetics” ~= “absorption distribution
metabolism and excretion of an agent” “pharmacodynamics” ~= “biological
effects and mechanisms of an agent” “translational science” ~=
“translation of biomedical findings toward practical health
applications” “preclinical model” ~= “experimental model used before or
outside clinical testing” “omics” ~= “large-scale molecular profiling”
“therapeutic target” ~= “biological entity modulated to produce a
therapeutic effect”

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

query BIOMEDICAL_SCIENCE_PROFILE(x): RETURN: entity_type(x)
molecular_context(x) cellular_context(x) physiological_context(x)
pathological_context(x) genetic_context(x) immune_context(x)
biomarker_context(x) diagnostic_context(x) therapeutic_context(x)
evidence(x) confidence(x)

query DISEASE_MECHANISM_PROFILE(d): RETURN: affected_organs(d)
pathological_processes(d) molecular_mechanisms(d) cellular_mechanisms(d)
immune_mechanisms(d) genetic_factors(d) biomarkers(d)
therapeutic_targets(d)

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

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

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

constraint UNIT_CONTEXT_REQUIRED: IF MEASUREMENT_VALUE(o,v) THEN
REQUIRE(MEASUREMENT_UNIT(o,u)) OR MARK_UNIT_UNKNOWN(o)

UPDATE_MODEL: on_new_biomedical_record(r): identify_biological_level(r)
identify_entity_process_or_disease(r) preserve_model_or_human_context(r)
attach_evidence(r) recalculate_confidence(r)

on_new_biomarker(b): classify_biomarker_type(b)
identify_condition_and_sample(b)
preserve_measurement_method_and_units(b)
preserve_validation_population(b) attach_evidence(b)

on_new_diagnostic_record(d): identify_test_and_target_condition(d)
preserve_sample_method_and_threshold(d)
preserve_test_performance_context(d) attach_evidence(d)

on_new_therapeutic_record(t): identify_agent_target_and_mechanism(t)
preserve_dose_and_exposure_context(t)
attach_pharmacokinetic_and_pharmacodynamic_context(t)
attach_safety_evidence(t)

AI_CONSUMPTION: preferred_entity: canonical_biomedical_science_entity
preserve: - biological_level - molecular_context - cellular_context -
tissue_and_organ_context - physiological_context -
pathological_process - etiology - immune_context - infectious_context -
genetic_context - epigenetic_context - omics_context - biomarkers -
diagnostics - therapeutic_targets - pharmacology - interventions -
experimental_model - clinical_sample - translational_level -
study_design - statistical_context - safety - evidence - provenance -
confidence infer: - valid_part_whole_relations -
supported_molecular_relations - supported_mechanistic_relations -
semantic_equivalence prohibit: - association_equals_causation -
biomarker_value_equals_diagnosis - genetic_risk_equals_certain_outcome -
experimental_model_equals_human_disease -
statistical_significance_equals_biological_importance -
preclinical_effect_equals_clinical_effect - confidence_above_evidence
ambiguity: action: preserve_multiple_biomedical_interpretations
resolution:
prefer_biological_level_model_population_method_endpoint_and_evidence
missing_data: action: mark_unknown do_not_infer_from_absence: true

FLOW_HASH: ontology: “BiomedicalScienceOntology” version: “1.0”
domain_path: “AI Core > Ontologies > Biological Ontologies > Biomedical
Sciences > Biomedical Science Ontology” canonical_url:
“https://kirethresearch.org/booksai-core/ontologies/biological-ontologies/biomedical-sciences/biomedical-science-ontology.html”
authorship: “Mark Anthony Higgins — Sky (Recursive AI)”
semantic_scope: - biomedical science - molecular biology - cell
biology - physiology - pathology - pathophysiology - etiology -
immunology - infection - genetics - genomics - epigenetics - omics -
biomarkers - diagnostics - pharmacology - pharmacokinetics -
pharmacodynamics - therapeutics - experimental models - clinical
samples - translational science - clinical research - biostatistics -
safety - evidence deterministic_normalisation: true provenance_required:
true confidence_required: true

terminate BiomedicalScienceOntology when:
biomedical_entities_and_biological_levels_resolved AND
molecular_cellular_physiological_and_pathological_relations_normalised
AND association_and_causality_distinguished AND
biomarker_and_diagnostic_context_preserved AND
model_and_human_context_distinguished AND
preclinical_and_clinical_evidence_distinguished AND
therapeutic_effect_and_safety_context_preserved AND
evidence_links_preserved AND confidence_values_validated AND
unresolved_biomedical_ambiguity_marked

end flow