Pubmed Parser is a Python library for parsing the PubMed Open-Access (OA) subset
, MEDLINE XML repositories, and Entrez Programming Utilities (E-utils). It uses the lxml library to parse this information into a Python dictionary which can be easily used for research, such as in text mining and natural language processing pipelines.
For available APIs and details about the dataset, please see our wiki page or documentation page for more details. Below, we list some of the core funtionalities and code examples.
path provided to a function can be the path to a compressed or uncompressed XML file. We provide example files in the data folder.Below, we list available parsers from pubmed_parser.
We created a simple parser for the PubMed Open Access Subset where you can give an XML path or string to the function called parse_pubmed_xml which will return a dictionary with the following information:
full_title : article's titleabstract : abstractjournal : Journal namepmid : PubMed IDpmc : PubMed Central IDdoi : DOI of the articlepublisher_id : publisher IDauthor_list : list of authors with affiliation keys in the following format [['last_name_1', 'first_name_1', 'aff_key_1'],
['last_name_1', 'first_name_1', 'aff_key_2'],
['last_name_2', 'first_name_2', 'aff_key_1'], ...]
affiliation_list : list of affiliation keys and affiliation strings in the following format [['aff_key_1', 'affiliation_1'],
['aff_key_2', 'affiliation_2'], ...]
publication_year : publication yearsubjects : list of subjects listed in the article separated by semicolon. Sometimes, it only contains the type of the article, such as a research article, review proceedings, etc.import pubmed_parser as pp
dict_out = pp.parse_pubmed_xml(path)
The function parse_pubmed_references will process a Pubmed Open Access XML file and return a list of the PMIDs it cites. Each dictionary has keys as follows
pmid : PubMed ID of the articlepmc : PubMed Central ID of the articlearticle_title : title of cited articlejournal : journal namejournal_type : type of journalpmid_cited : PubMed ID of article that article citesdoi_cited : DOI of article that article citesyear : Publication year as it appears in the reference (may include letter suffix, e.g.2007a)dicts_out = pp.parse_pubmed_references(path) # return list of dictionary
The function parse_pubmed_caption can parse image captions from a given path to XML file. It will return reference index that you can refer back to actual images. The function will return list of dictionary which has following keys
pmid : PubMed IDpmc : PubMed Central IDfig_caption : string of captionfig_id : reference id for figure (use to refer in XML article)fig_label : label of the figuregraphic_ref : reference to image file name provided from Pubmed OAdicts_out = pp.parse_pubmed_caption(path) # return list of dictionary
For someone who might be interested in parsing the text surrounding a citation, the library also provides that functionality. You can use parse_pubmed_paragraph to parse text and reference PMIDs. This function will return a list of dictionaries, where each entry will have following keys:
pmid : PubMed IDpmc : PubMed Central IDtext : full text of the paragraphreference_ids : list of reference code within that paragraph.This IDs can merge with output from parse_pubmed_references .
section : section of paragraph (e.g. Background, Discussion, Appendix, etc.)dicts_out = pp.parse_pubmed_paragraph('data/6605965a.nxml', all_paragraph=False)
You can use parse_pubmed_table to parse table from XML file. This function will return list of dictionaries where each has following keys.
pmid : PubMed IDpmc : PubMed Central IDcaption : caption of the tablelabel : lable of the tabletable_columns : list of column nametable_values : list of values inside the tabletable_xml : raw xml text of the table (return if return_xml=True)dicts_out = pp.parse_pubmed_table('data/medline16n0902.xml.gz', return_xml=False)
MEDLINE XML has a different XML format than PubMed Open Access. The structure of XML files can be found in MEDLINE/PubMed DTD here. You can use the function parse_medline_xml to parse that format. This function will return list of dictionaries, where each element contains:
pmid : PubMed IDpmc : PubMed Central IDdoi : DOIother_id : Other IDs found, each separated by ;title : title of the articleabstract : abstract of the articleauthors : authors, each separated by ;mesh_terms : list of MeSH terms with corresponding MeSH ID, each separated by ; e.g. 'D000161:Acoustic Stimulation; D000328:Adult; ...publication_types : list of publication type list each separated by ; e.g. 'D016428:Journal Article'keywords : list of keywords, each separated by ;chemical_list : list of chemical terms, each separated by ;pubdate : Publication date. Defaults to year information only.journal : journal of the given papermedline_ta : this is abbreviation of the journal namenlm_unique_id : NLM unique identificationissn_linking : ISSN linkage, typically use to link with Web of Science datasetcountry : Country extracted from journal information fieldreference : string of PMID each separated by ; or list of references made to the articledelete : boolean if False means paper got updated so you might have twolanguages : list of languages, separated by ;vernacular_title: vernacular title. Defaults to empty string whenever non-available.XMLs for the same paper. You can delete the record of deleted paper because it got updated.
dicts_out = pp.parse_medline_xml('data/medline16n0902.xml.gz',
year_info_only=False,
nlm_category=False,
author_list=False,
reference_list=False) # return list of dictionary
To extract month and day information from PubDate, set year_info_only=True. We also allow parsing structured abstract and we can control display of each section or label by changing nlm_category argument.
Use parse_grant_id in order to parse MEDLINE grant IDs from XML file. This will return a list of dictionaries, each containing
pmid : PubMed IDgrant_id : Grant IDgrant_acronym : Acronym of grantcountry : Country where grant funding fromagency : Grant agencyIf no Grant ID is found, it will return None
You can use PubMed parser to parse XML file from E-Utilities using parse_xml_web . For this function, you can provide a single pmid as an input and get a dictionary with following keys
title : titleabstract : abstractjournal : journalaffiliation : affiliation of first authorauthors : string of authors, separated by ;year : Publication yearkeywords : keywords or MESH terms of the articledict_out = pp.parse_xml_web(pmid, save_xml=False)
The function parse_citation_web allows you to get the citations to a given PubMed ID or PubMed Central ID. This will return a dictionary which contains the following keys
pmc : PubMed Central IDpmid : PubMed IDdoi : DOI of the articlen_citations : number of citations for given articlespmc_cited : list of PMCs that cite the given PMCdict_out = pp.parse_citation_web(doc_id, id_type='PMC')
The function parse_outgoing_citation_web allows you to get the articles a given article cites, given a PubMed ID or PubMed Central ID. This will return a dictionary which contains the following keys
n_citations : number of cited articlesdoc_id : the document identifier givenid_type : the type of identifier given. Either 'PMID' or 'PMC'pmid_cited : list of PMIDs cited by the articledict_out = pp.parse_outgoing_citation_web(doc_id, id_type='PMID')
Identifiers should be passed as strings. PubMed Central ID's are default, and should be passed as strings without the 'PMC' prefix. If no citations are found, or if no article is found matching doc_id in the indicated database, it will return None.
You can install the most update version of the package directly from the repository
pip install git+https://github.com/titipata/pubmed_parser.git
or install recent release with PyPI using
pip install pubmed-parser
or clone the repository and install using pip
git clone https://github.com/titipata/pubmed_parser
pip install ./pubmed_parser
You can test your installation by running pytest --cov=pubmed_parser tests/ --verbose
in the root of the repository.
An example usage is shown as follows
import pubmed_parser as pp
path_xml = pp.list_xml_path('data') # list all xml paths under directory
pubmed_dict = pp.parse_pubmed_xml(path_xml[0]) # dictionary output
print(pubmed_dict)
{'abstract': u"Background Despite identical genotypes and ...",
'affiliation_list':
[['I1': 'Department of Biological Sciences, ...'],
['I2': 'Biology Department, Queens College, and the Graduate Center ...']],
'author_list':
[['Dennehy', 'John J', 'I1'],
['Dennehy', 'John J', 'I2'],
['Wang', 'Ing-Nang', 'I1']],
'full_title': u'Factors influencing lysis time stochasticity in bacteriophage \u03bb',
'journal': 'BMC Microbiology',
'pmc': '3166277',
'pmid': '21810267',
'publication_year': '2011',
'publisher_id': '1471-2180-11-174',
'subjects': 'Research Article'}
This is a snippet to parse all PubMed Open Access subset using PySpark 2.1
import os
import pubmed_parser as pp
from pyspark.sql import Row
path_all = pp.list_xml_path('/path/to/xml/folder/')
path_rdd = spark.sparkContext.parallelize(path_all, numSlices=10000)
parse_results_rdd = path_rdd.map(lambda x: Row(file_name=os.path.basename(x),
**pp.parse_pubmed_xml(x)))
pubmed_oa_df = parse_results_rdd.toDF() # Spark dataframe
pubmed_oa_df_sel = pubmed_oa_df[['full_title', 'abstract', 'doi',
'file_name', 'pmc', 'pmid',
'publication_year', 'publisher_id',
'journal', 'subjects']] # select columns
pubmed_oa_df_sel.write.parquet('pubmed_oa.parquet', mode='overwrite') # write dataframe
See scripts folder for more information.
and contributors
If you use Pubmed Parser, please cite it from JOSS as follows
Achakulvisut et al., (2020). Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset XML Dataset. Journal of Open Source Software, 5(46), 1979, https://doi.org/10.21105/joss.01979
or using BibTex
@article{Achakulvisut2020,
doi = {10.21105/joss.01979},
url = {https://doi.org/10.21105/joss.01979},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {46},
pages = {1979},
author = {Titipat Achakulvisut and Daniel Acuna and Konrad Kording},
title = {Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset XML Dataset},
journal = {Journal of Open Source Software}
}
We welcome contributions from anyone who would like to improve Pubmed Parser. You can create GitHub issues to discuss questions or issues relating to the repository. We suggest you to read our Contributing Guidelines before creating issues, reporting bugs, or making a contribution to the repository.
This package is developed in Konrad Kording's Lab at the University of Pennsylvania. We would like to thank reviewers and the editor from JOSS including tleonardi, timClicks, and majensen. They made our repository much better!
MIT License Copyright (c) 2015-2020 Titipat Achakulvisut, Daniel E. Acuna
FAQs
A python parser for Pubmed Open-Access Subset and MEDLINE XML repository
We found that pubmed-parser demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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