Biomedical Sciences

flow “BiomedicalSciencesOntology” v1.0

KIRETH SEMANTIC RESEARCH ARCHIVE

AI CORE > ONTOLOGIES > BIOLOGICAL ONTOLOGIES > BIOMEDICAL SCIENCES

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.html”

type BiologicalEntity type Molecule type Gene type Protein type Cell
type Tissue type Organ 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)

rule PART_INVERSE(a,b): IF PART_OF(a,b) THEN HAS_PART(b,a)

———- 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_REGULATES(a,target)
relation CELL_OF(cell,tissue) relation DIFFERENTIATES_INTO(cell,target)
relation CELL_SIGNAL_RECEIVES(cell,signal) relation
CELL_PROLIFERATES(cell,event) relation CELL_DIES_BY(cell,process)

enum CellDeathType: APOPTOSIS NECROSIS NECROPTOSIS PYROPTOSIS
FERROPTOSIS

relation HAS_CELL_DEATH_TYPE(process,type)

———- PHYSIOLOGY / PATHOLOGY ———-

relation TISSUE_OF(tissue,organ) relation ORGAN_FUNCTION(organ,function)
relation REGULATES(system,variable) 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_ASSOCIATED_WITH(disease,factor) relation
CAUSED_BY(disease,cause) relation HAS_SEVERITY(disease,value)

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

———- IMMUNITY / INFECTION ———-

relation IMMUNE_COMPONENT_RECOGNISES(component,target) relation
CYTOKINE_SIGNALS_TO(cytokine,cell) relation
INFLAMMATORY_RESPONSE_TO(system,stimulus) relation
IMMUNE_RESPONSE_TO(system,target,response) relation
ANTIBODY_BINDS_TO(antibody,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
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
CHROMATIN_ACCESSIBILITY(region,value) relation
PROTEIN_ABUNDANCE(sample,protein,value) relation
METABOLITE_ABUNDANCE(sample,metabolite,value) relation
PROTEIN_INTERACTION(a,b)

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)

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 SENSITIVITY(test,value)
relation SPECIFICITY(test,value) relation LIMIT_OF_DETECTION(test,value)

———- PHARMACOLOGY ———-

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 CONCENTRATION_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)

———- INTERVENTIONS ———-

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)

———- EXPERIMENTAL MODELS ———-

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))

———- RESEARCH / TRANSLATION ———-

relation EXPERIMENT_TESTS(experiment,hypothesis) relation
EXPERIMENT_USES(experiment,model) relation
HAS_CONTROL(experiment,control) relation
HAS_TREATMENT(experiment,treatment) relation
HAS_ENDPOINT(experiment,endpoint) relation RANDOMISED(experiment)
relation BLINDED(experiment)

enum ClinicalSampleType: BLOOD PLASMA SERUM URINE SALIVA CSF TISSUE
BIOPSY SWAB SPUTUM

relation HAS_SAMPLE_TYPE(sample,type) 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
VALIDATED_IN(finding,cohort) relation REPLICATED_IN(finding,study)

———- STATISTICS / SAFETY ———-

relation EFFECT_SIZE(study,value) relation
CONFIDENCE_INTERVAL(study,lower,upper) relation P_VALUE(study,value)
relation SAMPLE_SIZE(study,value) relation
STATISTICAL_MODEL(study,model) relation TOXIC_TO(agent,entity) relation
TOXICITY_ENDPOINT(agent,endpoint) relation
MAXIMUM_TOLERATED_DOSE(agent,value) relation SAFETY_SIGNAL(agent,signal)

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 IMAGING PHYSIOLOGICAL
PRECLINICAL ANIMAL_STUDY ORGANOID_STUDY CLINICAL_OBSERVATION
CLINICAL_TRIAL DIAGNOSTIC_STUDY SYSTEMATIC_REVIEW META_ANALYSIS
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 ———-

SEMANTIC_EQUIVALENCE: “biomedical sciences” ~= “scientific study of
biological mechanisms relevant to human health and 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 research” ~= “translation of biological
findings toward practical biomedical application” “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 MODEL ———-

query BIOMEDICAL_ENTITY_PROFILE(x): RETURN: entity_type(x)
biological_context(x) molecular_relations(x) cellular_relations(x)
physiological_relations(x) pathological_relations(x)
biomarker_relations(x) therapeutic_relations(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 BIOMARKER_PROFILE(b): RETURN: biomarker_type(b) condition(b)
sample_type(b) measurement_method(b) threshold(b) evidence(b)

query THERAPEUTIC_PROFILE(a): RETURN: targets(a) mechanism_of_action(a)
pharmacokinetics(a) pharmacodynamics(a) therapeutic_effects(a)
adverse_effects(a) evidence(a)

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

———- CONSISTENCY ———-

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)

constraint MODEL_CONTEXT_REQUIRED: IF experimental_model_assertion(a)
THEN PRESERVE_MODEL_TYPE_AND_LIMITATIONS(a)

constraint TRANSLATION_LEVEL_REQUIRED: IF biomedical_finding(f) THEN
PRESERVE_PRECLINICAL_OR_CLINICAL_LEVEL(f)

———- UPDATE MODEL ———-

UPDATE_MODEL: on_new_biomedical_record(r): identify_biological_level(r)
identify_entity_or_process(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_context(b)
attach_evidence(b)

on_new_therapeutic_agent(a): identify_target(a) identify_mechanism(a)
preserve_dose_and_exposure(a) attach_pk_pd_context(a)
attach_safety_evidence(a)

on_new_experimental_result(r): identify_model_and_endpoint(r)
preserve_controls_and_replication(r)
distinguish_model_result_from_human_effect(r) attach_evidence(r)

on_conflicting_biomedical_evidence(a,b): preserve(a) preserve(b)
compare_models(a,b) compare_populations(a,b) compare_methods(a,b)
compare_endpoints(a,b) flag_unresolved_conflict()

———- AI CONSUMPTION ———-

AI_CONSUMPTION: preferred_entity: canonical_biomedical_entity
preserve: - biological_level - molecular_context - cellular_context -
tissue_and_organ_context - physiological_context -
pathological_process - immune_context - infectious_context -
genetic_context - epigenetic_context - omics_context - biomarkers -
diagnostics - therapeutic_targets - pharmacology - interventions -
experimental_model - clinical_sample - translational_level -
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 ———-

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

terminate BiomedicalSciencesOntology when:
biomedical_entities_and_levels_resolved AND
molecular_cellular_physiological_and_pathological_relations_normalised
AND biomarker_and_diagnostic_context_preserved AND
association_and_causality_distinguished AND
model_and_human_context_distinguished AND
preclinical_and_clinical_evidence_distinguished AND
evidence_links_preserved AND confidence_values_validated AND
unresolved_biomedical_ambiguity_marked

end flow