【问题标题】:How can I extract the abstract from efetch (Biopython, Entrez)?如何从 efetch(Biopython,Entrez)中提取摘要?
【发布时间】:2016-03-18 15:02:04
【问题描述】:

我是 python 新手,想使用 bio 包中的 entrez 系统从 pubmed 中提取摘要。 我得到了 esearch 来给我我的 UID(存储在 my_list_ges),我还可以使用 efetch 下载一个条目。 然而,现在结果是一个字典列表,条目看起来像字典,但我无法访问它们:

Entrez.email= "my-email@provider.sth"
handle=Entrez.efetch(db="pubmed",id=my_list_ges[0],rettype="null",retmode="xml")
record = Entrez.read(handle)
abstract=record["Abstract"]
handle.close()

结果是一个类型错误:

TypeError: list indices must be integers, not str 

当我尝试从第一条记录中检索 'Abstract' 时,我得到一个 KeyError

>>> record[0]["Abstract"]
KeyError: 'Abstract'

这很奇怪,因为在搜索结果中,我可以通过字典轻松访问我的 UID

record[0]的结构是:

{u'MedlineCitation': DictElement({
        u'OtherID': [],
        u'OtherAbstract': [],
        u'CitationSubset': ['IM'],
        u'KeywordList': [],
        u'DateCreated': {u'Month': '03', u'Day': '17', u'Year': '2016'},
        u'SpaceFlightMission': [],
        u'GeneralNote': [],
        u'Article':
        DictElement({
            u'ArticleDate': [
                DictElement({u'Month': '03', u'Day': '16', u'Year': '2016'}, attributes={u'DateType': u'Electronic'})],
           u'Pagination': {u'MedlinePgn': 'e0151666'},
           u'AuthorList': ListElement([
            DictElement({
                u'LastName': "O'Neill",
                u'Initials': 'KE',
                u'Identifier': [],
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}], 
                u'ForeName': 'Kathy E'
                }, attributes={u'ValidYN': u'Y'}),
            DictElement({
                u'LastName': 'Bredenkamp',
                u'Initials': 'N', u'Identifier': [],
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}],
                u'ForeName': 'Nicholas'}, attributes={u'ValidYN': u'Y'}), 
            DictElement({
                u'LastName': 'Tischner',
                u'Initials': 'C',
                u'Identifier': [], 
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}],
                u'ForeName': 'Christin'}, attributes={u'ValidYN': u'Y'}),
            DictElement({
                u'LastName': 'Vaidya',
                u'Initials': 'HJ',
                u'Identifier': [],
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}],
                u'ForeName': 'Harsh J'}, attributes={u'ValidYN': u'Y'}),
            DictElement({
                u'LastName': 'Stenhouse',
                u'Initials': 'FH',
                u'Identifier': [],
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}], u'ForeName': 'Frances H'}, attributes={u'ValidYN': u'Y'}),
            DictElement({
                u'LastName': 'Peddie',
                u'Initials': 'CD',
                u'Identifier': [],
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}],
                u'ForeName': 'C Diana'}, attributes={u'ValidYN': u'Y'}),
            DictElement({
                u'LastName': 'Nowell',
                u'Initials': 'CS',
                u'Identifier': [],
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}],
                u'ForeName': 'Craig S'}, attributes={u'ValidYN': u'Y'}),
            DictElement({
                u'LastName': 'Gaskell', 
                u'Initials': 'T', 
                u'Identifier': [], 
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}],
                u'ForeName': 'Terri'}, attributes={u'ValidYN': u'Y'}),
            DictElement({
                u'LastName': 'Blackburn',
                u'Initials': 'CC',
                u'Identifier': [],
                u'AffiliationInfo': [{
                    u'Affiliation': 'MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, SCRM Building, 5 Little France Drive, Edinburgh, EH16 4UU, UK.',
                    u'Identifier': []}], u'ForeName': 'C Clare'}, attributes={u'ValidYN': u'Y'})],
            attributes={u'Type': u'authors', u'CompleteYN': u'Y'}),
        u'Language': ['eng'],
        u'PublicationTypeList': [StringElement('Journal Article', attributes={u'UI': u'D016428'})],
        u'Journal': {
            u'ISSN': StringElement('1932-6203', attributes={u'IssnType': u'Electronic'}),
            u'ISOAbbreviation': 'PLoS ONE',
            u'JournalIssue': DictElement({
                u'Volume': '11',
                u'Issue': '3',
                u'PubDate': {u'Year': '2016'}}, attributes={u'CitedMedium': u'Internet'}),
            u'Title': 'PloS one'},
        u'ArticleTitle': 'Foxn1 Is Dynamically Regulated in Thymic Epithelial Cells during Embryogenesis and at the Onset of Thymic Involution.',
        u'ELocationID': [StringElement('10.1371/journal.pone.0151666', attributes={u'ValidYN': u'Y', u'EIdType': u'doi'})],
        u'Abstract': {u'AbstractText': ['--Unnecessarily long abstract removed --']}}, attributes={u'PubModel': u'Electronic-eCollection'}),
        u'PMID': StringElement('26983083', attributes={u'Version': u'1'}),
        u'MedlineJournalInfo': {
            u'MedlineTA': 'PLoS One',
            u'Country': 'United States',
            u'NlmUniqueID': '101285081',
            u'ISSNLinking': '1932-6203'}}, attributes={u'Owner': u'NLM', u'Status': u'In-Data-Review'}),
 u'PubmedData': {
    u'ArticleIdList': [
        StringElement('10.1371/journal.pone.0151666', attributes={u'IdType': u'doi'}),
        StringElement('PONE-D-15-47173', attributes={u'IdType': u'pii'}),
        StringElement('26983083', attributes={u'IdType': u'pubmed'})],
    u'PublicationStatus': 'epublish',
    u'History': [
        DictElement({u'Month': '', u'Day': '', u'Year': '2016'}, attributes={u'PubStatus': u'ecollection'}),
        DictElement({u'Month': '10', u'Day': '28', u'Year': '2015'}, attributes={u'PubStatus': u'received'}),
        DictElement({u'Month': '3', u'Day': '2', u'Year': '2016'}, attributes={u'PubStatus': u'accepted'}),
        DictElement({u'Month': '3', u'Day': '16', u'Year': '2016'}, attributes={u'PubStatus': u'epublish'}),
        DictElement({u'Minute': '0', u'Month': '3', u'Day': '17', u'Hour': '6', u'Year': '2016'}, attributes={u'PubStatus': u'entrez'}),
        DictElement({u'Minute': '0', u'Month': '3', u'Day': '18', u'Hour': '6', u'Year': '2016'}, attributes={u'PubStatus': u'pubmed'}),
        DictElement({u'Minute': '0', u'Month': '3', u'Day': '18', u'Hour': '6', u'Year': '2016'}, attributes={u'PubStatus': u'medline'})]}
}

【问题讨论】:

  • 在我看来Abstract 并不直接在record[0] 中,它在嵌套记录之一中,具体来说是record[0]['MedlineCitation']['Abstract']

标签: python biopython pubmed


【解决方案1】:

我发现返回 Medline 记录并对其进行解析要容易得多。我为相关查询插入了完整的工作代码:query = "Tischner[AU] Cortex-specific down-regulation"下面代码中的重点是fetch_rec()函数使用rettype='Medline', retmode='text',然后使用BioPython的Medline模块解析结果记录。

from StringIO import StringIO
from Bio import Entrez, Medline

def search_medline(query, email):
    Entrez.email = email
    search = Entrez.esearch(db='pubmed', term=query, usehistory='y')
    handle = Entrez.read(search)
    try:
        return handle
    except Exception as e:
        raise IOError(str(e))
    finally:
        search.close()

def fetch_rec(rec_id, entrez_handle):
    fetch_handle = Entrez.efetch(db='pubmed', id=rec_id,
                                 rettype='Medline', retmode='text',
                                 webenv=entrez_handle['WebEnv'],
                                 query_key=entrez_handle['QueryKey'])
    rec = fetch_handle.read()
    return rec

def main(query, email):
    rec_handler = search_medline(query, email)

    for rec_id in rec_handler['IdList']:
        rec = fetch_rec(rec_id, rec_handler)
        rec_file = StringIO(rec)
        medline_rec = Medline.read(rec_file)
        if 'AB' in medline_rec:
            print(medline_rec['AB'])

if __name__ == '__main__':
    email = "my-email@provider.sth"
    query = "Tischner[AU] Cortex-specific down-regulation"
    main(query, email)

它会打印出您要查找的摘要,但随着query 参数的更改,此脚本可能适用于任何搜索。有更有效的方法可以提取大量记录,但对于小型搜索,这将是可行的。

【讨论】:

  • 谢谢高登,这对我有用。但是,我想下载大约一百万个条目。不过,对于较少的条目,这是完美的。
  • 我明白了,@MaxS。批量下载有简单的方法,但需要重写 fetch 函数。我会尽量找时间更新它以处理该用例,如果只是为了将来其他人使用。
  • 谢谢,非常好。保罗解决这个问题的方式与我的代码一致。我使用了 esearch 和 efetch 并将 retmax agument 增加到 100 000 并在 retstart 上进行了交互
  • 我完全同意并且我打算遵循这种模式。比较哪种方法更快会很有趣,尽管超过一百万条记录在使用条款的限制内下载小批量记录所花费的时间将超过解析所花费的任何时间。值得检查。我会玩的:)
【解决方案2】:

我不太了解在这种情况下应该做的“正确”事情是什么(不熟悉 biopython),但你得到 KeyError 的原因在于 'Abstract' 键嵌套在'MedlineCitation'字典:

record[0]['MedlineCitation']['Article']['Abstract']

应该给你类似的东西:

{'AbstractText': ['--Unnecessarily long abstract removed --']}

【讨论】:

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