Examples

Advanced Search Patterns

Complex PubMed Queries

from metapub import PubMedFetcher

fetch = PubMedFetcher()

# Search with date range
pmids = fetch.pmids_for_query(
    query='cancer treatment',
    since='2020/01/01',
    until='2023/12/31',
    retmax=100
)

# Search specific journal
pmids = fetch.pmids_for_query(
    journal='Nature',
    year=2023,
    retmax=50
)

# PMC-only articles
pmids = fetch.pmids_for_query(
    query='open access',
    pmc_only=True,
    retmax=25
)

Citation Lookup

from metapub import PubMedFetcher

fetch = PubMedFetcher()

# Find article by citation details
pmids = fetch.pmids_for_citation(
    jtitle='Nature',
    year=2023,
    volume=615,
    first_page=123,
    aulast='Smith'
)

Working with Related Articles

from metapub import PubMedFetcher

fetch = PubMedFetcher()

# Get related articles
related_pmids = fetch.related_pmids('12345678')

print(f"Found {len(related_pmids)} related articles")

for pmid in related_pmids[:5]:  # First 5
    article = fetch.article_by_pmid(pmid)
    print(f"{pmid}: {article.title}")

ClinVar Integration

from metapub import ClinVarFetcher

cv = ClinVarFetcher()

# Search for variants in a gene
variants = cv.variants_for_gene('BRCA1')

for variant in variants:
    print(f"Accession: {variant.accession}")
    print(f"Clinical Significance: {variant.clinical_significance}")
    print(f"Gene: {variant.gene_symbol}")

Batch Processing

Processing Multiple PMIDs

from metapub import PubMedFetcher
import csv

fetch = PubMedFetcher()
pmids = ['12345678', '23456789', '34567890']

# Export to CSV
with open('articles.csv', 'w', newline='') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow(['PMID', 'Title', 'Journal', 'Year', 'DOI'])

    for pmid in pmids:
        try:
            article = fetch.article_by_pmid(pmid)
            writer.writerow([
                pmid,
                article.title,
                article.journal,
                article.year,
                article.doi
            ])
        except Exception as e:
            print(f"Error processing {pmid}: {e}")

Text Mining

Extracting DOIs from Text

from metapub.text_mining import find_doi_in_string

text = """The study (doi:10.1038/nature12373) shows that...
Another reference is https://doi.org/10.1126/science.1234567"""

dois = find_doi_in_string(text)
print(f"Found DOIs: {dois}")

Finding PMIDs in Text

from metapub.text_mining import find_pmid_in_string

text = "See PMID: 12345678 and also PMID 23456789 for details."

pmids = find_pmid_in_string(text)
print(f"Found PMIDs: {pmids}")

Caching and Performance

Custom Cache Directory

from metapub import PubMedFetcher
import os

# Set custom cache directory
os.environ['METAPUB_CACHE_DIR'] = '/path/to/my/cache'

fetch = PubMedFetcher()
# Subsequent requests will use the custom cache

API Key Configuration

import os
from metapub import PubMedFetcher

# Set API key for higher rate limits
os.environ['NCBI_API_KEY'] = 'your_api_key_here'

fetch = PubMedFetcher()
# Now you can make more requests per second

Error Handling

Handling Network Issues

from metapub import PubMedFetcher
from metapub.exceptions import MetaPubError

fetch = PubMedFetcher()

try:
    article = fetch.article_by_pmid('12345678')
    print(article.title)
except MetaPubError as e:
    print(f"Metapub error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Validating PMIDs

from metapub.validate import is_valid_pmid

pmids = ['12345678', 'invalid', '23456789']

valid_pmids = [pmid for pmid in pmids if is_valid_pmid(pmid)]
print(f"Valid PMIDs: {valid_pmids}")