## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----setup--------------------------------------------------------------------
# library(unitcm)

## ----herbs-search-------------------------------------------------------------
# # Text search
# herbs <- search_herbs(q = "ginseng")
# 
# # Multi-value faceted filters
# herbs <- search_herbs(
#   flavors = c("sweet", "bitter"),
#   properties = "warm",
#   toxicity = "non-toxic"
# )
# 
# # View available filter values
# facets <- fetch_herb_facets()
# facets$toxicity
# facets$meridians

## ----herbs-detail-------------------------------------------------------------
# herb <- get_herb("UNITCM_H001")
# herb$herb_english_name
# herb$efficacy
# 
# # Get compounds found in this herb
# compounds <- get_herb_compounds("UNITCM_H001", all_pages = TRUE)
# head(compounds)

## ----herbs-export-------------------------------------------------------------
# export_herbs(q = "ginseng", file = "ginseng_herbs.csv")
# export_herb_compounds("UNITCM_H001", file = "ginseng_compounds.csv")

## ----compounds-search---------------------------------------------------------
# # Drug-like compounds with molecular weight 200-500
# compounds <- search_compounds(
#   mw_min = 200, mw_max = 500,
#   lipinski = "pass",
#   is_drug = TRUE
# )
# 
# # Get facet statistics
# facets <- fetch_compound_facets()
# facets$mw_range

## ----compounds-detail---------------------------------------------------------
# # Full compound record
# compound <- get_compound("UNITCM_I00001")
# compound$component_name
# compound$xref$pubchem_cid
# 
# # ADMET predictions (~90 endpoints)
# admet <- get_compound_admet("UNITCM_I00001")
# admet$caco2_permeability
# admet$hia
# 
# # Predicted targets (DrugCLIP deep learning)
# targets_dc <- get_compound_targets("UNITCM_I00001", method = "drugclip")
# 
# # ChEMBL similarity-based targets
# targets_ch <- get_compound_targets("UNITCM_I00001", method = "chembl")
# 
# # Both sources combined
# targets_all <- get_compound_targets("UNITCM_I00001", method = "both")
# table(targets_all$source)

## ----formulas-search----------------------------------------------------------
# # Search by text
# formulas <- search_formulas(q = "insomnia")
# 
# # Filter by ICD-11 classification
# formulas <- search_formulas(
#   level1 = "Neoplasms",
#   mapping_confidence = c("high", "medium")
# )
# 
# # Browse the disease classification tree
# tree <- fetch_disease_tree()
# 
# # Available filter options
# list_book_sources()
# list_origin_sources()
# list_dosage_forms()

## ----formulas-detail----------------------------------------------------------
# formula <- get_formula(1)
# formula$formula_name
# formula$efficacy
# 
# # Herb composition and dosage
# doses <- get_formula_doses(1)
# doses

## ----ontology-search----------------------------------------------------------
# # Full-text search
# results <- search_ontology("Qi stagnation")
# 
# # Get entity with relations
# entity <- get_ontology_entity("TCM_0001")
# entity$ancestors
# entity$children
# entity$external_mappings
# 
# # Navigate the hierarchy
# children <- get_ontology_children("TCM_0001")
# ancestors <- get_ontology_ancestors("TCM_0001")
# descendants <- get_ontology_descendants("TCM_0001", max_level = 2)

## ----ontology-tree------------------------------------------------------------
# # Fetch tree at depth 2
# tree <- fetch_ontology_tree(depth = 2)
# 
# # Statistics
# stats <- fetch_ontology_stats()
# stats$total_entities
# 
# # Top-level categories
# categories <- list_ontology_categories()

## ----ontology-mapping---------------------------------------------------------
# # Find TCM entities mapped to a MeSH term
# mapped <- search_ontology_mapping("MeSH", "D008516")
# 
# # Export ontology
# export_ontology("csv", file = "tcm_ontology.csv")
# export_ontology("json", depth = 3, file = "tcm_ontology.json")

## ----midas-basic--------------------------------------------------------------
# # Which diseases are associated with these genes?
# g2d <- query_gene_diseases(c("TP53", "BRCA1", "EGFR"))
# g2d
# attr(g2d, "gene_mapping")
# 
# # Which genes are associated with breast cancer?
# d2g <- query_disease_genes("breast cancer")
# d2g
# attr(d2g, "matched_diseases")

## ----midas-enrichment---------------------------------------------------------
# gene_list <- c("TP53", "BRCA1", "EGFR", "VEGFA", "MYC", "KRAS")
# enrichment <- query_disease_enrichment(
#   gene_list,
#   p_value_cutoff = 0.05,
#   correction_method = "fdr"
# )
# enrichment
# attr(enrichment, "total_significant")

## ----midas-utils--------------------------------------------------------------
# # Convert mixed gene identifiers
# convert_gene_ids(c("TP53", "7157", "ENSG00000141510"))
# 
# # Autocomplete disease names
# autocomplete_disease("breast")
# 
# # Available data sources
# fetch_midas_sources()
# 
# # Database statistics
# fetch_midas_stats()