PLOS Biology-Inspired PLOS Biology Articles

This past week I had my first encounter with the concept of graph databases which lend themselves perfectly to modeling and capturing linked data.

I started reading the free and brilliant book Graph Databases by Robinson, Webber, and Eifrem and began playing around with Python bulbs by James Thornton.

I further took the data set of 1754 PLOS Biology articles that I have examined on this blog multiple times and created a Rexster-based graph database from them. Apart from the obvious authors, DOIs, and titles I also extracted references to other PLOS Biology articles.

In this blog post I will examine these links between PLOS Biology articles.

The Graph Database

Let us first take a look at my database to get an idea of what this looks like.

%matplotlib inline
from matplotlib import pyplot


from bulbs.rexster import Graph, Config, REXSTER_URI


REXSTER_URI = 'http://localhost:8182/graphs/plos'
config = Config(REXSTER_URI)


g = Graph(config)

The label g now holds a reference to our graph database.

Python bulbs allows us to define classes for our data model which is something I did when creating this graph database in the first place.

Our Graph Model

These are the node (vertex) types and edge (relationship) types I defined:

# Bulbs Models
from bulbs.model import Node, Relationship
from bulbs.property import String, Integer, DateTime, List

class Author(Node):
    element_type = 'author'
    name = String(nullable=False)
    
class Article(Node):
    element_type = 'article'
    title = String(nullable=False)
    published = DateTime()
    doi = String()
    
class Authorship(Relationship):
    label = 'authored'

class Citation(Relationship):
    label = 'cites'
    reference_count = Integer(nullable=False)
    tag = String()

This is a very basic model of PLOS Biology articles that captures nothing more than authorship (edges between authors and articles) and citations (edges between articles).

Some of these concepts can and should probably be decorated further: for instance Authorship edges could include author contributions (as provided at the bottom of most PLOS Biology articles).

g.add_proxy('authors', Author)
g.add_proxy('articles', Article)
g.add_proxy('authored', Authorship)
g.add_proxy('cites', Citation)

Querying the Database

Usually we would use Rexster/Bulbs-builtin functions that rely on some internal index but since that index seems to be broken for me right now I will simply collect all nodes and edges by hand and create Python dictionaries as indeces.

This is okay here to do since our database is very small but would likely be prohibitive for anything marginally bigger.

nodes = g.V
edges = g.E


authors = {n.name: n for n in nodes if n.element_type == 'author'}

Here are ten authors in our database:

authors.keys()[:10]

[u'Shuguang Zhang',
 u'Ernst Hafen',
 u'Maren Brockmeyer',
 u'Bruno Eschli',
 u'David B. Gurevich',
 u'Michael Lynch',
 u'Alejandro Valbuena',
 u'Claudia Rutte',
 u'Matthew M Wyatt',
 u'Brianna B. Williams']



articles = {n.doi: n for n in nodes if n.element_type == 'article'}

And here are the DOI’s of ten articles in our database:

articles.keys()[:10]

[u'10.1371/journal.pbio.0040216',
 u'10.1371/journal.pbio.0040215',
 u'10.1371/journal.pbio.0040210',
 u'10.1371/journal.pbio.0040368',
 u'10.1371/journal.pbio.0040369',
 u'10.1371/journal.pbio.0040362',
 u'10.1371/journal.pbio.0040363',
 u'10.1371/journal.pbio.0040360',
 u'10.1371/journal.pbio.0020275',
 u'10.1371/journal.pbio.0040366']

Let us now do a brief sanity check and count the number of PLOS Biology articles in our data set (this should equal 1754).

len(articles.keys())

1754

Examining One Article

Let us now pick an article at random and see how this article is connected to the remainder of the graph.

article = articles['10.1371/journal.pbio.1000584']

This is the title of article:

article.title

u'Clusters of Temporal Discordances Reveal Distinct Embryonic Patterning Mechanisms in Drosophila and Anopheles'

These are the edges pointing to this article:

list(article.inE())

[<Authorship: http://localhost:8182/graphs/plos/edges/21895>,
 <Authorship: http://localhost:8182/graphs/plos/edges/21893>,
 <Authorship: http://localhost:8182/graphs/plos/edges/21891>]

There are three Authorship edges that point to this specific article.

To get the node at the base of a directed edge we can either query article.inE().inV() (i.e. the in-node of this edge) or simply ask for the in-node of the article node straight away - this should be equivalent!

for author in article.inV():
    print author.name

Yury Goltsev
Michael Levine
Dmitri Papatsenko

A quick check online confirms that these are indeed the authors of article.

As I mentioned above, I also collected all references to other PLOS Biology articles in my data set and modeled those as Citation relationships (edges) between articles.

The article we are currently looking at has one such out-edge to another PLOS Biology article:

list(article.outE())

[<Citation: http://localhost:8182/graphs/plos/edges/29290>]

And these are the title, authors, and DOI of the article cited by article:

for citation in article.outV():
    print citation.title
    print [n.name for n in citation.inV() if n.element_type == 'author']
    print citation.doi

The Cell Cycle–Regulated Genes of Schizosaccharomyces pombe
[u'Saumyadipta Pyne', u'Janet Leatherwood', u'Anna Oliva', u'Bruce Futcher', u'Adam Rosebrock', u'Steve Skiena', u'Francisco Ferrezuelo', u'Haiying Chen']
10.1371/journal.pbio.0030225

As you can see above, querying our database for the authors of the PLOS Biology article that our current article (article) cites is simple.

Number of PLOS Biology Articles That Cite Other PLOS Biology Articles

How many PLOS Biology articles in our data set of 1754 articles cite other PLOS Biology articles?

(caveat: this only represents those citations that I detected when parsing my set of articles)

sum(1 for n in nodes if n.element_type == 'article' and n.outV() > 0)

526

I did not only extract citation edges between PLOS Biology articles but also counted how often such a citation occurs in the body of the article.

For our article and its one cited PLOS Biology article I counted:

for citation in article.outE():
    print citation.reference_count

2

Just to verify this, look up article online (DOI = 10.1371/journal.pbio.0030225) and look for reference [4] which corresponds to this one cited PLOS Biology article.

Distribution of PLOS Biology-Internal References

Allowing Self-Citation

Let us now take a look at the observed distribution of how often cited PLOS Biology articles are referenced in the main text of the citing PLOS Biology article.

citation_counts = []
for doi in articles.keys():
    if articles[doi].outE():
        for e in articles[doi].outE():
            if e.label == 'cites':
                citation_counts.append(e.reference_count)


pyplot.hist(citation_counts, bins=range(20))
pyplot.xlabel('number of times cited')
pyplot.ylabel('count')

<matplotlib.text.Text at 0x9a293d0>

png

This histogram has a surprisingly long tail. Let us take a look at some of the bigger values to see if these make sense.

def article_pp(article):
    authors = unicode(', '.join([n.name for n in article.inV() if n.element_type == 'author']))
    s = ('Title: %s\n'
        'Authors: %s\n'
        'DOI: %s' % (article.title, authors, article.doi))
    
    return s


for edge in edges:
    if edge.label == 'cites':
        if edge.reference_count >= 21:
            print('Citer:')
            print(article_pp(edge.outV()))
            print('')
            print('Citee')
            print(article_pp(edge.inV()))
            print('')
            print('Citer cites citee %d times.' % edge.reference_count)
            print('-----------------------------------------------------')

Citer:

Title: A Feedback Loop between Dynamin and Actin Recruitment during Clathrin-Mediated Endocytosis

Authors: Marko Lampe, Christien J. Merrifield, Marcus J. Taylor

DOI: 10.1371/journal.pbio.1001302

Citee:

Title: A High Precision Survey of the Molecular Dynamics of Mammalian Clathrin-Mediated Endocytosis

Authors: Marcus J. Taylor, David Perrais, Christien J. Merrifield

DOI: 10.1371/journal.pbio.1000604

Citer cites citee 21 times.


Citer:

Title: H2A.Z-Mediated Localization of Genes at the Nuclear Periphery Confers Epigenetic Memory of Previous Transcriptional State

Authors: Yvonne Fondufe-Mittendorf, Sara Ahmed, Jason H Brickner, Donna Garvey Brickner, Jonathan Widom, Ivelisse Cajigas, Pei-Chih Lee

DOI: 10.1371/journal.pbio.0050081

Citee:

Title: Gene Recruitment of the Activated INO1 Locus to the Nuclear Membrane

Authors: Peter Walter, Jason H Brickner

DOI: 10.1371/journal.pbio.0020342

Citer cites citee 21 times.


Checking these by hand we verify that our counts are correct.

I think it is sensible to postulate that the more often one article cites another one, the more heavily the work presented in the citer was influenced by the citee.

There is certainly some cut-off at which importance stops increasing - my point is simply that citing another article multiple times in your manuscript probably means that you are basing your work at least partially on the article you cite.

Disallowing Self-Citation

For both pairs above we can see that at least one author is shared between citer and citee.

One question I am interested in is: How inspiring is the work of one group of authors for a completely different group of authors?

In my opinion, if one group of authors inspires a completely different group of authors to carry out scientific work (be it to follow up, refute, or whatever) then that defines knowledge transfer and a point at which scientific knowledge really becomes worth the time and resources it cost to produce this knowledge in the first place.

(I am certain this statement can be refined further but roughly speaking this is what I think)

Let us redo the above histogram but exclude all pairs of citers and citees that share one or more authors.

(one more bracketed caveat: When constructing my database I assumed that every author name occurs exactly once and is therefore unique - this is a heuristic that breaks easily)

def are_different_authors(article_1, article_2):
    authors_1 = []
    authors_2 = []
    
    for n in article_1.inV():
        if n.element_type == 'author':
            authors_1.append(n.name)
    for n in article_2.inV():
        if n.element_type == 'author':
            authors_2.append(n.name)
            
    authors_1 = set(authors_1)
    authors_2 = set(authors_2)
    
    return len(authors_1.intersection(authors_2)) == 0



citation_counts = []

for edge in edges:
    if edge.label == 'cites':
        if are_different_authors(edge.inV(), edge.outV()):
            citation_counts.append(edge.reference_count)


pyplot.hist(citation_counts, bins=range(20))
pyplot.xlabel('number of times cited')
pyplot.ylabel('count')

<matplotlib.text.Text at 0xaa25310>

png

This histogram does not look very different from the one above.

Let us take a look at data points in the tail:

for edge in edges:
    if edge.label == 'cites':
        if edge.reference_count >= 16 and are_different_authors(edge.inV(), edge.outV()):
            print('Citer:')
            print(article_pp(edge.outV()))
            print('')
            print('Citee')
            print(article_pp(edge.inV()))
            print('')
            print('Citer cites citee %d times.' % edge.reference_count)
            print('-----------------------------------------------------')

Citer:

Title: Lack of Support for the Association between GAD2 Polymorphisms and Severe Human Obesity

Authors: Frank Geller, John P Kane, Raphael Merriman, Christian Vaisse, Winfried Rief, Robert Dent, Johannes Hebebrand, Björn Waldenmaier, Franck Mauvais-Jarvis, Anke Hinney, Michael M Swarbrick, Clive R Pullinger, Mary Malloy, Len A Pennacchio, Anna Ustaszewska, Denise L Lind, Wen-Chi Hsueh, Ruth McPherson, Martha M Cavazos, André Scherag, Pui-Yan Kwok

DOI: 10.1371/journal.pbio.0030315

Citee:

Title: GAD2 on Chromosome 10p12 Is a Candidate Gene for Human Obesity

Authors: Lynn Bekris, Valérie Vasseur-Delannoy, Philippe Boutin, Karin Séron, Philippe Froguel, Mohamed Chikri, Christian Dina, Laetitia Corset, M. Aline Charles, Séverine Dubois, Francis Vasseur, Janice Cabellon, Ake Lernmark, Bernadette Neve, Karine Clement

DOI: 10.1371/journal.pbio.0000068

Citer cites citee 17 times.


Citer:

Title: Structural Basis of Rap Phosphatase Inhibition by Phr Peptides

Authors: Alberto Marina, Francisca Gallego del Sol

DOI: 10.1371/journal.pbio.1001511

Citee:

Title: Structural Basis of Response Regulator Inhibition by a Bacterial Anti-Activator Protein

Authors: Matthew B. Neiditch, Melinda D. Baker

DOI: 10.1371/journal.pbio.1001226

Citer cites citee 16 times.


As we can see the two article pairs in the tail of this updated distribution are linked with lower reference counts than what we observed before filtering for author disjointedness.

The Most Inspiring PLOS Biology Articles (PLOS Biology-Internally)

Now, how inspiring are PLOS Biology authors for other (different) PLOS Biology authors?

To answer this question, I would like to propose a measure that I have called Inspiration Factor in my own head for some time now and one variant of the model I have had in mind is this:

Inspiration is an increasing function of the number of authors (unrelated to you) that you inspired to carry out scientific work.

Since I do not want to count citations that are mentioned only once in the main text of an article, I will impose a threshold of at least three references.

I should refine the way I parse articles to account for the context that citations are referenced in.

Anyways, let us take a look at those PLOS Biology articles that have inspired at least three other PLOS Biology articles.

inspirators = []

for article in articles.values():
    in_nodes = []
    if article.inE():
        for edge in article.inE():
            if edge.label == 'cites':
                if are_different_authors(edge.inV(), edge.outV()) and edge.reference_count >= 3:
                    in_nodes.append([edge.outV(), edge.reference_count])
                    
    if len(in_nodes) >= 3:
        inspirators.append([article, in_nodes])

And this is the number of PLOS Biology articles in my data set that have been referenced by three or more PLOS Biology articles at least three times (i.e. these are our inspirators):

len(inspirators)

2

And here our our inspirators in more detail:

for inspirator in inspirators:
    print('Inspirator')
    print article_pp(inspirator[0])
    print('')
    for el in inspirator[1]:
        print('Inspired Article')
        print article_pp(el[0])
        print('Cites inspirator %d times.' % el[1])
        print('')
    print('--------------------------------------')
    print('')

Edit: Upon closer inspection I realize that my method of distinguishing authors, implemented in are_different_authors, is too simplistic to properly distinguish authors in my data set. You will find that the names of some authors are spelled with and without abbreviation periods such as Alexander D Johnson versus Alexander D. Johnson. Amending are_different_authors to catch these spelling variations shows that both inspirators listed below are not inspirators as defined by my arbitrary categories. Rather than updating are_different_authors with some ad-hoc fix I will think about this issue more coherently and leave the misclassified articles below to show that author disambiguation is not easy.

Inspirator:

Title: The Evolution of Combinatorial Gene Regulation in Fungi

Authors: Alexander D Johnson, Aaron D Hernday, Hao Li, Brian B Tuch, David J Galgoczy

DOI: 10.1371/journal.pbio.0060038

Inspired Article:

Title: Biofilm Matrix Regulation by Candida albicans Zap1

Authors: Oliver R. Homann, Clarissa J. Nobile, Jean-Sebastien Deneault, Aaron P. Mitchell, Andre Nantel, Aaron D. Hernday, David R. Andes, Jeniel E. Nett, Alexander D. Johnson

DOI: 10.1371/journal.pbio.1000133

Cites inspirator 3 times.

Inspired Article:

Title: Evolutionary Tinkering with Conserved Components of a Transcriptional Regulatory Network

Authors: Jaideep Mallick, Adnane Sellam, Hugo Lavoie, Hervé Hogues, Malcolm Whiteway, André Nantel

DOI: 10.1371/journal.pbio.1000329

Cites inspirator 6 times.

Inspired Article

Title: Evolution of Phosphoregulation: Comparison of Phosphorylation Patterns across Yeast Species

Authors: Assen Roguev, Dorothea Fiedler, Jonathan C. Trinidad, Wendell A. Lim, Pedro Beltrao, Kevan M. Shokat, Alma L. Burlingame, Nevan J. Krogan

DOI: 10.1371/journal.pbio.1000134

Cites inspirator 3 times.


Inspirator:

Title: Transcription Factors Bind Thousands of Active and Inactive Regions in the Drosophila Blastoderm

Authors: Lisa Simirenko, Michael B Eisen, Mark Stapleton, Richard Weiszmann, Cris L. Luengo Hendriks, Tom Gingeras, Amy Beaton, Hou Cheng Chu, Xiao-yong Li, Terence P Speed, Victor Sementchenko, Mark D Biggin, Richard Bourgon, Stewart MacArthur, William Inwood, Susan E Celniker, Nobuo Ogawa, Venky N Iyer, David W Knowles, Daniel A Pollard, David Nix, Aaron Hechmer

DOI: 10.1371/journal.pbio.0060027

Inspired Article:

Title: Target Genes of the MADS Transcription Factor SEPALLATA3: Integration of Developmental and Hormonal Pathways in the Arabidopsis Flower

Authors: Cezary Smaczniak, Kerstin Kaufmann, Pawel Krajewski, Ruy Jauregui, Chiara A Airoldi, Gerco C Angenent, Jose M Muiño

DOI: 10.1371/journal.pbio.1000090

Cites inspirator 3 times.

Inspired Article:

Title: Evolutionary Plasticity of Polycomb/Trithorax Response Elements in Drosophila Species

Authors: Arne Hauenschild, Leonie Ringrose, Renato Paro, Christina Altmutter, Marc Rehmsmeier

DOI: 10.1371/journal.pbio.0060261

Cites inspirator 6 times.

Inspired Article:

Title: Quantitative Analysis of the Drosophila Segmentation Regulatory Network Using Pattern Generating Potentials

Authors: Sudhir Kumar, Susan E. Celniker, Ann S. Hammonds, Saurabh Sinha, Majid Kazemian, Charles Blatti, Noriko Wakabayashi-Ito, Scot A. Wolfe, Adam Richards, Michael McCutchan, Michael H. Brodsky

DOI: 10.1371/journal.pbio.1000456

Cites inspirator 7 times.


Outlook: Inspiration Chains

And that is it for now.

I will expand my data set to include more articles and think about how to enrich the data I extract from these articles.

One question that I am very intrigued to tackle soon is: How long of a chain of scientific discovery do you trigger?

I imagine that an article that lies at the beginning of a long chain of articles that inspired one another would have some significance.

comments powered by Disqus