SeungpilJeong_PubMed_Trend

Overview

The script is designed to analyze trends in multiomics data analysis methods by counting the number of publications related to specific keywords from the PubMed database over a range of years (2011-2024). The results are then saved into a CSV file.

Key Components

1. Keywords and Targets:

   • base_keywords: A list of base keywords related to omics.
   • targets: A list of specific analysis methods and networks to search for.

2. Filename:

   • The results will be saved in a CSV file named based on the base_keywords.

3. Imports:

   • requests: Used to make HTTP requests to the PubMed API.
   • csv: Used to write the results to a CSV file.

4. API Setup:

   • BASE_URL and SEARCH_URL: Base URL and search endpoint for the PubMed API.
   • DB: Specifies the PubMed database.

Functions

1. formulate_query:

   • This function constructs a search query string by combining the keywords and optionally a publication year.
   • It joins keywords with " AND " to ensure all terms must be present in the search results.

   def formulate_query(keywords, year=None):
       query = " AND ".join(f"({keyword})" for keyword in keywords)
       if year:
           query += f" AND (\"{year}\"[Date - Publication])"
       return query

2. get_count_for_year_and_target:

   • This function generates a query for a specific year and target, makes a request to the PubMed API, and returns the count of search results.
   • It adds the target keyword to the base keywords and constructs the query.
   • It then sends a request to the PubMed API and extracts the count of results from the JSON response.

   def get_count_for_year_and_target(year, target):
       keywords = [target, base_keywords[0], base_keywords[1], base_keywords[2]]
       query = formulate_query(keywords, year)
       params = {
           "db": DB,
           "term": query,
           "retmode": "json"
       }
       response = requests.get(SEARCH_URL, params=params)
       data = response.json()
       return int(data["esearchresult"]["count"])

Main Script

1. Initialize Results Dictionary:

   • A dictionary results is created to store the publication counts for each target and year.

   results = {year: {} for year in range(2011, 2024)}

2. Fetch Data:

   • For each target, the script loops through each year and populates the results dictionary with the publication counts.

   for target in targets:
       for year in range(2011, 2024):
           results[year][target] = get_count_for_year_and_target(year, target)

3. Write Results to CSV:

   • The script opens a CSV file for writing.
   • It writes the header row, which includes "Year" and all target methods.
   • It writes the data rows, each corresponding to a year, followed by the publication counts for each target.

   with open(filename, 'w', newline='') as csvfile:
       csvwriter = csv.writer(csvfile)
       header = ['Year'] + targets
       csvwriter.writerow(header)
       for year in results:
           row = [year] + [results[year][target] for target in targets]
           csvwriter.writerow(row)

4. Final Output:

   • The script prints a confirmation message indicating where the results have been saved.

   print(f"Results saved to {filename}")

Summary

This script automates the process of querying PubMed for the number of publications related to various multiomics analysis methods over a span of years. It organizes this data into a CSV file, making it easier to analyze trends over time.

The script prints a confirmation message indicating where the results have been saved. print(f"Results saved to {filename}") Summary This script automates the process of querying PubMed for the number of publications related to various multiomics analysis methods over a span of years. It organizes this data into a CSV file, making it easier to analyze trends over time.