Motivation¶
The primary motivation for putting together Citehound was the sheer amount of repetitive tasks we found ourselves having to do while trying to process bibliographical data.
Why analyse bibliographical data?¶
Starting from scratch¶
There is no better demonstrator of the motivating factors behind Citehound than reviewing a small snippet of all the attributes that are associated with an academic paper in the Pubmed data base.
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"https://dtd.nlm.nih.gov/ncbi/pubmed/out/pubmed_190101.dtd">
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<PMID Version="1">33302541</PMID>
<DateCompleted>
<Year>2021</Year>
<Month>04</Month>
<Day>15</Day>
</DateCompleted>
<DateRevised>
<Year>2021</Year>
<Month>04</Month>
<Day>15</Day>
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<Article PubModel="Electronic">
<Journal>
<ISSN IssnType="Electronic">1420-3049</ISSN>
<JournalIssue CitedMedium="Internet">
<Volume>25</Volume>
<Issue>24</Issue>
<PubDate>
<Year>2020</Year>
<Month>Dec</Month>
<Day>08</Day>
</PubDate>
</JournalIssue>
<Title>Molecules (Basel, Switzerland)</Title>
<ISOAbbreviation>Molecules</ISOAbbreviation>
</Journal>
<ArticleTitle>Comprehensive Review on Alzheimer's Disease: Causes and Treatment.</ArticleTitle>
<ELocationID EIdType="pii" ValidYN="Y">E5789</ELocationID>
<ELocationID EIdType="doi" ValidYN="Y">10.3390/molecules25245789</ELocationID>
<Abstract>
<AbstractText>Alzheimer's disease (AD) is a disorder that causes degeneration of the cells in the
brain and it is the main cause of dementia, which is characterized by a decline in thinking and
independence in personal daily activities. AD is considered a multifactorial disease: two main
hypotheses were proposed as a cause for AD, cholinergic and amyloid hypotheses. Additionally,
several risk factors such as increasing age, genetic factors, head injuries, vascular diseases,
infections, and environmental factors play a role in the disease. Currently, there are only two
classes of approved drugs to treat AD, including inhibitors to cholinesterase enzyme and
antagonists to <i>N</i>-methyl d-aspartate (NMDA), which are effective only in treating the
symptoms of AD, but do not cure or prevent the disease. Nowadays, the research is focusing on
understanding AD pathology by targeting several mechanisms, such as abnormal tau protein
metabolism, β-amyloid, inflammatory response, and cholinergic and free radical damage,
aiming to develop successful treatments that are capable of stopping or modifying the course of
AD. This review discusses currently available drugs and future theories for the development of
new therapies for AD, such as disease-modifying therapeutics (DMT), chaperones, and natural
compounds.
</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y">
<Author ValidYN="Y">
<LastName>Breijyeh</LastName>
<ForeName>Zeinab</ForeName>
<Initials>Z</Initials>
<Identifier Source="ORCID">0000-0003-1233-7826</Identifier>
<AffiliationInfo>
<Affiliation>Pharmaceutical Sciences Department, Faculty of Pharmacy, Al-Quds University,
Jerusalem 20002, Palestine.
</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Karaman</LastName>
<ForeName>Rafik</ForeName>
<Initials>R</Initials>
<Identifier Source="ORCID">0000-0001-5526-4490</Identifier>
<AffiliationInfo>
<Affiliation>Pharmaceutical Sciences Department, Faculty of Pharmacy, Al-Quds University,
Jerusalem 20002, Palestine.
</Affiliation>
</AffiliationInfo>
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<Language>eng</Language>
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<Year>2020</Year>
<Month>12</Month>
<Day>08</Day>
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<Country>Switzerland</Country>
<MedlineTA>Molecules</MedlineTA>
<NlmUniqueID>100964009</NlmUniqueID>
<ISSNLinking>1420-3049</ISSNLinking>
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<RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D016875">tau Proteins</NameOfSubstance>
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<MeshHeadingList>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D000544">Alzheimer Disease</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000175">diagnosis</QualifierName>
<QualifierName MajorTopicYN="Y" UI="Q000209">etiology</QualifierName>
<QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName>
<QualifierName MajorTopicYN="Y" UI="Q000628">therapy</QualifierName>
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<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D016229">Amyloid beta-Peptides</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D000818">Animals</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D015415">Biomarkers</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D019468">Disease Management</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D004198">Disease Susceptibility</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D019636">Neurodegenerative Diseases</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000209">etiology</QualifierName>
<QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName>
<QualifierName MajorTopicYN="N" UI="Q000628">therapy</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D018570">Risk Assessment</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D012307">Risk Factors</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D012720">Severity of Illness Index</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D016875">tau Proteins</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName>
</MeshHeading>
</MeshHeadingList>
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<Keyword MajorTopicYN="N">Alzheimer’s disease</Keyword>
<Keyword MajorTopicYN="N">chaperons</Keyword>
<Keyword MajorTopicYN="N">disease-modifying therapy</Keyword>
<Keyword MajorTopicYN="N">heat shock proteins</Keyword>
<Keyword MajorTopicYN="N">neurodegeneration</Keyword>
<Keyword MajorTopicYN="N">risk factors</Keyword>
<Keyword MajorTopicYN="N">tau protein</Keyword>
<Keyword MajorTopicYN="N">β-amyloid peptide</Keyword>
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<Year>2020</Year>
<Month>11</Month>
<Day>05</Day>
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<PubMedPubDate PubStatus="revised">
<Year>2020</Year>
<Month>12</Month>
<Day>03</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted">
<Year>2020</Year>
<Month>12</Month>
<Day>06</Day>
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<PubMedPubDate PubStatus="entrez">
<Year>2020</Year>
<Month>12</Month>
<Day>11</Day>
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<Year>2021</Year>
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<Day>16</Day>
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<ArticleId IdType="doi">10.3390/molecules25245789</ArticleId>
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The most natural reaction to anyone who may have browsed a Pubmed XML file with a view to analysing its contents, is to go:
“Alright, let’s write a script to extract the data we are after”.
There is absolutely no doubt that simple Python programs can be written to parse and extract some parameters of interest out of these data and analyse it.
The “fun” bit starts when those first simple things have showed their potential and now as investigators we start asking for more parameters, more inference, more accuracy and so on. Suddenly, that XML Python script with the 72 ad-hoc additions it suffered on its way to publishing that one “proof-of-concept” paper shows its limitations.
Even existing tools leave a lot of work up to the researcher especially when it comes to pre-processing and data linkage.
Background to the MeSH hierarchy¶
Every Pubmed academic journal article entry has one or more subject descriptors associated with it, describing the topic(s) that a given paper is dealing with. Here is an extract from an academic journal entry to show what these descriptors look like:
<?xml version="1.0" ?>
<!DOCTYPE PubmedArticleSet PUBLIC "-//NLM//DTD PubMedArticle, 1st January 2019//EN"
"https://dtd.nlm.nih.gov/ncbi/pubmed/out/pubmed_190101.dtd">
<PubmedArticleSet>
<PubmedArticle>
...
...
<MeshHeadingList>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D000544">Alzheimer Disease</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000175">diagnosis</QualifierName>
<QualifierName MajorTopicYN="Y" UI="Q000209">etiology</QualifierName>
<QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName>
<QualifierName MajorTopicYN="Y" UI="Q000628">therapy</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D016229">Amyloid beta-Peptides</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D000818">Animals</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D015415">Biomarkers</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D019468">Disease Management</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D004198">Disease Susceptibility</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D019636">Neurodegenerative Diseases</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000209">etiology</QualifierName>
<QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName>
<QualifierName MajorTopicYN="N" UI="Q000628">therapy</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D018570">Risk Assessment</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D012307">Risk Factors</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D012720">Severity of Illness Index</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName MajorTopicYN="N" UI="D016875">tau Proteins</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName>
</MeshHeading>
</MeshHeadingList>
...
...
...
...
</PubmedArticle>
</PubmedArticleSet>
Each one of these descriptor “codes” (e.g. D000544) points to a fully documented term in the MeSH hierarchy.
There is tremendous value in these descriptors because they are assigned by human beings and because the descriptors themselves form a hierarchy. Here is what this hierarchy looks like from the MeSH browser:
Fig. 1 A branch of the MeSH tree showing the hierarchical organisation of the subject descriptors.¶
The motivation for including the ability to actively use these descriptors for querying data within Citehound is the ability to form queries that can collect articles from “subject generalisations” by following the branches of the MeSH tree to higher levels.
For example, given the “Aminoacids, Peptides and Proteins” branch of the MeSH tree depicted in
figure Fig. 1, it is extremely straightforward to recall all papers that
have a particular descriptor attached to them. You just search for all articles that include the D016229 descriptor.
But with the availability of the hierarchy, given a descriptor it is possible to retrieve the MeSH hierarchy “tree location identifier”, use that identifier to move up the hierarchy a level (to the more general subject), retrieve the descriptors that describe the general subject and finally all of the papers that are in the same general subject as a given paper.
Having access to the complete hierarchy might seem like an overkill, given that the tree identifier is self-describing.
That is, given D12.644.024, it is already known that 024 is a specialisation of the 644 which is a
specialisation of the D12 branch.
But, this is not the whole story. The same MeSH descriptor can belong to two branches of the MeSH tree but, more importantly, the MeSH hierarchy is a dynamic network.
This changes everything.
Over time, new codes come into existence and older codes are withdrawn or, worse even, get merged or re-assigned. For
example, the term Blockchain was only established in 2019
and if you try to search for D003293 (also known as “Convulsions”) in the 2021 version of the MeSH tree, you
will not find that code.
And yet, D003293 was being assigned to papers between the years of 2002 and 2004 when the tree was re-organised.
What this means is that if your search covers a long enough span (e.g. 3-5 years), your static search queries, simply referencing a code, will be inaccurate.
How do we know?
In our research we came across this type of “problems” with codes very often, especially when trying to be very specific (e.g. in rare diseases). It was already expected that a given query would return just a few results but trying to expand this search with alternatives was now inhibited by the fact that the MeSH hierarchy was changing throughout the time span that a given search was covering.
To counter-act this we needed to know how does the MeSH tree was changing over time. That is, which year a new code was introduced, which year it was removed, which year it was modified (and how) and so on.
This is why the actual data importing process detailed further below, is split into two parts.
