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{
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{
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"execution": {
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"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Essential_Thrombocythemia\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Essential_Thrombocythemia/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Essential_Thrombocythemia/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Essential_Thrombocythemia/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Essential_Thrombocythemia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "4be07e45",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"id": "75e0b835",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Available TCGA subdirectories: ['TCGA_Liver_Cancer_(LIHC)', 'TCGA_Lower_Grade_Glioma_(LGG)', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Mesothelioma_(MESO)', 'TCGA_Ocular_melanomas_(UVM)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Rectal_Cancer_(READ)', 'TCGA_Sarcoma_(SARC)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Thymoma_(THYM)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Uterine_Carcinosarcoma_(UCS)', '.DS_Store', 'CrawlData.ipynb', 'TCGA_Acute_Myeloid_Leukemia_(LAML)', 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Glioblastoma_(GBM)', 'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)']\n",
"Selected directory: TCGA_Adrenocortical_Cancer_(ACC)\n",
"Clinical data file: ../../input/TCGA/TCGA_Adrenocortical_Cancer_(ACC)/TCGA.ACC.sampleMap_ACC_clinicalMatrix\n",
"Genetic data file: ../../input/TCGA/TCGA_Adrenocortical_Cancer_(ACC)/TCGA.ACC.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Clinical data columns:\n",
"['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'atypical_mitotic_figures', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'clinical_M', 'ct_scan_findings', 'cytoplasm_presence_less_than_equal_25_percent', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'diffuse_architecture', 'distant_metastasis_anatomic_site', 'excess_adrenal_hormone_diagnosis_method_type', 'excess_adrenal_hormone_history_type', 'form_completion_date', 'gender', 'germline_testing_performed', 'histologic_disease_progression_present_indicator', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'invasion_of_tumor_capsule', 'is_ffpe', 'laterality', 'lost_follow_up', 'lymph_node_examined_count', 'metastatic_neoplasm_confirmed_diagnosis_method_name', 'metastatic_neoplasm_confirmed_diagnosis_method_text', 'mitoses_count', 'mitotane_therapy', 'mitotane_therapy_adjuvant_setting', 'mitotane_therapy_for_macroscopic_residual_disease', 'mitotic_rate', 'necrosis', 'new_neoplasm_confirmed_diagnosis_method_name', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'nuclear_grade_III_IV', 'number_of_lymphnodes_positive_by_he', 'oct_embedded', 'other_dx', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'post_surgical_procedure_assessment_thyroid_gland_carcinoma_stats', 'postoperative_rx_tx', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'radiation_therapy', 'residual_tumor', 'ret', 'sample_type', 'sample_type_id', 'sinusoid_invasion', 'therapeutic_mitotane_levels_achieved', 'therapeutic_mitotane_lvl_macroscopic_residual', 'therapeutic_mitotane_lvl_progression', 'therapeutic_mitotane_lvl_recurrence', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weiss_score', 'weiss_venous_invasion', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_ACC_mutation_curated_bcm_gene', '_GENOMIC_ID_TCGA_ACC_exp_HiSeqV2_percentile', '_GENOMIC_ID_data/public/TCGA/ACC/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_ACC_miRNA_HiSeq', '_GENOMIC_ID_TCGA_ACC_RPPA', '_GENOMIC_ID_TCGA_ACC_hMethyl450', '_GENOMIC_ID_TCGA_ACC_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_ACC_exp_HiSeqV2', '_GENOMIC_ID_TCGA_ACC_gistic2thd', '_GENOMIC_ID_TCGA_ACC_PDMRNAseq', '_GENOMIC_ID_TCGA_ACC_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_ACC_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_ACC_gistic2', '_GENOMIC_ID_TCGA_ACC_mutation_bcm_gene', '_GENOMIC_ID_TCGA_ACC_mutation_curated_broad_gene']\n",
"\n",
"Clinical data shape: (92, 104)\n",
"Genetic data shape: (20530, 79)\n"
]
}
],
"source": [
"import os\n",
"\n",
"# Step 1: Look for directories related to Adrenocortical Cancer\n",
"tcga_subdirs = os.listdir(tcga_root_dir)\n",
"print(f\"Available TCGA subdirectories: {tcga_subdirs}\")\n",
"\n",
"# Look for directory related to Adrenocortical Cancer\n",
"target_dir = None\n",
"for subdir in tcga_subdirs:\n",
" # Look for exact match or synonymous terms\n",
" if trait.lower() in subdir.lower() or \"ACC\" in subdir:\n",
" target_dir = subdir\n",
" break\n",
"\n",
"if target_dir is None:\n",
" print(f\"No suitable directory found for {trait}.\")\n",
" # Mark the task as completed by creating a JSON record indicating data is not available\n",
" validate_and_save_cohort_info(is_final=False, cohort=\"TCGA\", info_path=json_path, \n",
" is_gene_available=False, is_trait_available=False)\n",
" exit() # Exit the program\n",
"\n",
"# Step 2: Get file paths for the selected directory\n",
"cohort_dir = os.path.join(tcga_root_dir, target_dir)\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"print(f\"Selected directory: {target_dir}\")\n",
"print(f\"Clinical data file: {clinical_file_path}\")\n",
"print(f\"Genetic data file: {genetic_file_path}\")\n",
"\n",
"# Step 3: Load clinical and genetic data\n",
"clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\\t')\n",
"genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\\t')\n",
"\n",
"# Step 4: Print column names of clinical data\n",
"print(\"\\nClinical data columns:\")\n",
"print(clinical_df.columns.tolist())\n",
"\n",
"# Additional basic information\n",
"print(f\"\\nClinical data shape: {clinical_df.shape}\")\n",
"print(f\"Genetic data shape: {genetic_df.shape}\")\n"
]
},
{
"cell_type": "markdown",
"id": "817b5e19",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "efc2fcbd",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [58, 44, 23, 23, 30], 'days_to_birth': [-21496, -16090, -8624, -8451, -11171]}\n",
"Gender columns preview:\n",
"{'gender': ['MALE', 'FEMALE', 'FEMALE', 'FEMALE', 'MALE']}\n"
]
}
],
"source": [
"# Identify candidate age and gender columns\n",
"candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n",
"candidate_gender_cols = ['gender']\n",
"\n",
"# Load the clinical data file path\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'TCGA_Adrenocortical_Cancer_(ACC)'))\n",
"\n",
"# Load the clinical data\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Extract and preview age columns\n",
"if candidate_age_cols:\n",
" age_preview = {col: clinical_df[col].head(5).tolist() for col in candidate_age_cols}\n",
" print(\"Age columns preview:\")\n",
" print(age_preview)\n",
"\n",
"# Extract and preview gender columns\n",
"if candidate_gender_cols:\n",
" gender_preview = {col: clinical_df[col].head(5).tolist() for col in candidate_gender_cols}\n",
" print(\"Gender columns preview:\")\n",
" print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "078f1952",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1529ce68",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Evaluating age columns:\n",
" age_at_initial_pathologic_diagnosis: [58, 44, 23, 23, 30], Missing: 0.0%\n",
" days_to_birth: [-21496, -16090, -8624, -8451, -11171], Missing: 0.0%\n",
"Evaluating gender columns:\n",
" gender: ['MALE', 'FEMALE', 'FEMALE', 'FEMALE', 'MALE'], Missing: 0.0%\n",
"\n",
"Chosen age column: age_at_initial_pathologic_diagnosis\n",
"Chosen gender column: gender\n"
]
}
],
"source": [
"# Examine potential age columns\n",
"print(\"Evaluating age columns:\")\n",
"for col_name, values in {'age_at_initial_pathologic_diagnosis': [58, 44, 23, 23, 30], 'days_to_birth': [-21496, -16090, -8624, -8451, -11171]}.items():\n",
" missing_count = sum(1 for v in values if v is None)\n",
" missing_percentage = missing_count / len(values) * 100 if values else 0\n",
" print(f\" {col_name}: {values}, Missing: {missing_percentage}%\")\n",
"\n",
"# Examine potential gender columns\n",
"print(\"Evaluating gender columns:\")\n",
"for col_name, values in {'gender': ['MALE', 'FEMALE', 'FEMALE', 'FEMALE', 'MALE']}.items():\n",
" missing_count = sum(1 for v in values if v is None)\n",
" missing_percentage = missing_count / len(values) * 100 if values else 0\n",
" print(f\" {col_name}: {values}, Missing: {missing_percentage}%\")\n",
"\n",
"# Select the best columns for age and gender\n",
"age_col = \"age_at_initial_pathologic_diagnosis\" # Contains direct age values which are easier to interpret\n",
"gender_col = \"gender\" # Contains standard gender information\n",
"\n",
"# Print the chosen columns\n",
"print(f\"\\nChosen age column: {age_col}\")\n",
"print(f\"Chosen gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "82b0ab8c",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
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"id": "2f30cf98",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/TCGA.csv\n",
"Clinical data shape: (92, 3)\n",
" Essential_Thrombocythemia Age Gender\n",
"sampleID \n",
"TCGA-OR-A5J1-01 1 58 1\n",
"TCGA-OR-A5J2-01 1 44 0\n",
"TCGA-OR-A5J3-01 1 23 0\n",
"TCGA-OR-A5J4-01 1 23 0\n",
"TCGA-OR-A5J5-01 1 30 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Essential_Thrombocythemia/gene_data/TCGA.csv\n",
"Normalized gene data shape: (19848, 79)\n",
"Linked data shape: (79, 19851)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After handling missing values - linked data shape: (79, 19851)\n",
"Quartiles for 'Essential_Thrombocythemia':\n",
" 25%: 1.0\n",
" 50% (Median): 1.0\n",
" 75%: 1.0\n",
"Min: 1\n",
"Max: 1\n",
"The distribution of the feature 'Essential_Thrombocythemia' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 35.0\n",
" 50% (Median): 49.0\n",
" 75%: 59.5\n",
"Min: 14\n",
"Max: 77\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '1' with 31 occurrences. This represents 39.24% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"After removing biased features - linked data shape: (79, 19851)\n",
"Linked data not saved due to quality concerns\n"
]
}
],
"source": [
"# Step 1: Extract and standardize the clinical features\n",
"# Get file paths\n",
"cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Adrenocortical_Cancer_(ACC)')\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"# Load data\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Create standardized clinical features dataframe with trait, age, and gender\n",
"# The trait for Adrenocortical Cancer is based on tumor/normal classification\n",
"clinical_features = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=trait, # Using predefined trait variable\n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\n",
"\n",
"# Save clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"print(f\"Clinical data shape: {clinical_features.shape}\")\n",
"print(clinical_features.head())\n",
"\n",
"# Step 2: Normalize gene symbols in gene expression data\n",
"# Transpose the genetic data to have genes as rows\n",
"genetic_data = genetic_df.copy()\n",
"\n",
"# Normalize gene symbols using the NCBI Gene database synonyms\n",
"normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)\n",
"\n",
"# Save normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"\n",
"# Step 3: Link clinical and genetic data\n",
"# Transpose genetic data to get samples as rows, genes as columns\n",
"genetic_data_transposed = normalized_gene_data.T\n",
"\n",
"# Ensure clinical and genetic data have the same samples (index values)\n",
"common_samples = clinical_features.index.intersection(genetic_data_transposed.index)\n",
"clinical_subset = clinical_features.loc[common_samples]\n",
"genetic_subset = genetic_data_transposed.loc[common_samples]\n",
"\n",
"# Combine clinical and genetic data\n",
"linked_data = pd.concat([clinical_subset, genetic_subset], axis=1)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 4: Handle missing values\n",
"linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
"print(f\"After handling missing values - linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 5: Determine biased features\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)\n",
"print(f\"After removing biased features - linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 6: Validate data quality and save cohort info\n",
"# First check if we have both gene and trait data\n",
"is_gene_available = linked_data.shape[1] > 3 # More than just trait, Age, Gender\n",
"is_trait_available = trait in linked_data.columns\n",
"\n",
"# Take notes of special findings\n",
"notes = f\"TCGA Adrenocortical Cancer dataset processed. Used tumor/normal classification as the trait.\"\n",
"\n",
"# Validate the data quality\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=notes\n",
")\n",
"\n",
"# Step 7: Save linked data if usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Linked data not saved due to quality concerns\")"
]
}
],
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