COVIDGraph: Connecting biomedical COVID-19 resources and
computational biology models
Lea Gütebier Ron Henkel Alexander Jarasch
University Medicine Greifswald University Medicine Greifswald German Center for Diabetes Research
Greifswald, Germany Greifswald, Germany Munich, Germany
lea.guetebier@stud.uni-greifswald.de ron.henkel@uni-greifswald.de jarasch@dzd-ev.de
Tim Bleimehl Sebastian Müller Jamie Munro
German Center for Diabetes Research yWorks Munro Consulting
Munich, Germany Tübingen, Germany London, UK
tim.bleimehl@helmholtz- sebastian.mueller@yworks.com jamie@munro.consulting
muenchen.de
Martin Preusse, and the Dagmar Walthemath
HealthEcco Team University Medicine Greifswald
Kaiser & Preusse Greifswald, Germany
Freiburg, Germany dagmar.waltemath@uni-
martin@kaiser-preusse.com greifswald.de
ABSTRACT 1 INTRODUCTION
The COVID-19 pandemic has changed life across the globe. In Jan- CovidGraph is a research and communication platform that encom-
uary 2020, little was known about SARS-COV-2, but the vastly passes publications, case statistics, genes and functions, molecular
increasing number of infections and the uncontrolled spreading data and more. It is developed and maintained by HealthECCO, a
demanded fast medical action. Within a year, over 4 million publi- non-profit collaboration of researchers, software developers, data
cations relating to COVID-19 appeared in the scientific literature. scientists and medical professionals (https://healthecco.org/). Our
Additionally, patents have been registered, ontologies have been aim is to help researchers quickly and efficiently find their way
extended, simulation studies for prediction of disease spread and through COVID-19 datasets using tools that implement artificial
underlying bioinformatics mechanisms have been built, and health intelligence methods, advanced visualisation techniques, and intu-
studies have been designed. To support the exploration of COVID- itive user interfaces. Through CovidGraph users can explore papers,
19 data, the CovidGraph project was initiated as a non-profit, collab- patents, treatments and medications covering the family of corona
orative and open project driven by researchers, software developers, viruses. In addition to literature data we connect information from
data scientists and medical professionals. In this article we outline biological entities - namely genes, proteins and their function -
the history, goals and scope of CovidGraph. Using the example of spanning a network of unparalleled size and knowledge. The latest
computational biology models, we show how additional resources addition to the CovidGraph are systems biology models (Fig. 1).
can be integrated with the knowledge graph to extend the scope of
the CovidGraph, for example, to systems biology data.
Reference Format:
Lea Gütebier, Ron Henkel, Alexander Jarasch, Tim Bleimehl, Sebastian
Müller, Jamie Munro, Martin Preusse, and the HealthEcco Team,
and Dagmar Walthemath. COVIDGraph: Connecting biomedical
COVID-19 resources and computational biology models. In the 2nd
Workshop on Search, Exploration, and Analysis in Heterogeneous
Datastores (SEA Data 2021).
PVLDB Artifact Availability:
The source code, data, and/or other artefacts have been made available at
https://github.com/covidgraph/documentation.
Copyright © 2021 for the individual papers by the papers’ authors. Copyright © 2021
for the volume as a collection by its editors. This volume and its papers are published
under the Creative Commons License Attribution 4.0 International (CC BY 4.0).
Published in the Proceedings of the 2nd Workshop on Search, Exploration, and Anal- Figure 1: Overview: CovidGraph data sources with the inte-
ysis in Heterogeneous Datastores, co-located with VLDB 2021 (August 16-20, 2021,
Copenhagen, Denmark) on CEUR-WS.org. grated system biology nodes (cyan box).
Over the last years, NoSQL approaches such as Key-Value Stores, to the Reactome pathway knowledgebase, a database for molec-
BigTable, document databases, triple stores, or graph databases ular information about biological pathways [11]. As components
[1], together with semantic web applications, became more pop- of the transcription and translation process in humans genes code
ular within the life sciences. Graph databases offer a storage con- for transcripts which in turn code for proteins. In the CovidGraph
cept based on nodes, (directed) edges, properties and labels. Nodes these processes are described by relationships between gene nodes,
can be labelled and are connected by edges, and both can con- transcript nodes and protein nodes. The data for the transcript
tain properties. They also allow easy horizontal scaling and fast nodes is taken from the NCBI Reference Sequence Database [17];
graph traversal. Finally, graph databases are schema optional – the Universal Protein Resource (UniProt) provides a resource of
a feature that is much appreciated when storing heterogeneous, protein sequences and annotation data [5]. Proteins associated with
highly connected, cross-domain data items from different sources. annotation data from the Gene Ontology are linked to GO term
The HealthECCO project integrates such heterogeneous resources nodes. The last node type connected with gene nodes are disease
and compiles a knowledge-base targeted at COVID-19 data (https: nodes. They are in turn associated with anatomy nodes. The cor-
//healthecco.org/covidgraph/), and potentially other diseases in responding data is provided by Hetionet, an integrative network
future versions. The underlying graph database is Neo4j [18]. of biomedical data including connections between diseases and
anatomies [9].
Knowledge is primarily centred around the domain of corona-
2 DATA RESOURCES viruses but is steadily extended to other connected diseases as part
Previous versions of the CovidGraph already integrated data from of the HealthECCO project. The latest addition to CovidGraph is a
five categories (Fig. 2 (A)): Patents, Papers, BioMedical (ontolo- resource of computational biology models. We will introduce the
gies and controlled vocabularies), Clinical Trials and Statistical & systems biology node in detail in Section 4.
Geographic. Categories are cross-linked by relationships. For ex-
ample, items from the "Papers" category are linked to items from 3 COVIDGRAPH FRAMEWORK
the "Patents" category. One paper source is the COVID-19 Open Re-
The CovidGraph infrastructure is built as a labelled property graph
search Dataset (CORD-19) – a collection of research papers relating
based on the Neo4j Enterprise edition v4.2. Textual information,
to COVID-19 (and corona viruses) [24]. It is the main data source
such as publications, clinical studies or ontology term descriptions,
for information about papers in the CovidGraph and contains pub-
is enriched and recognised by a pipeline based on natural lan-
lications from PubMed, medRxiv and bioRxiv. Papers and related
guage processing and named entity recognition (BioBERT [13]).
information are stored and linked in multiple nodes in the Covid-
The graph, as of now, contains 36 million nodes and 59 million re-
Graph. Each paper node has author nodes connected to affiliation
lationships but is still growing as the modular software framework
nodes that, in turn, are linked to location nodes. Papers can be linked
encourages to add and integrate new data sources. Server-wise,
to COVID-19 patents. The Lens (https://about.lens.org/covid-19/)
CovidGraph relies on Docker Container. To integrate a new data
provides datasets of patent documents and literature concerning hu-
source, it needs to be wrapped in a container and it needs to pro-
man corona viruses and COVID-19. The CovidGraph furthermore
vide information such as connection data and mapping information
contains information about clinical COVID-19 studies from the
(https://github.com/covidgraph/data_template). An ETL-process
ClinicalTrials.gov registry. Studies are represented as clinical trials
(https://git.connect.dzd-ev.de/dzdtools/motherlode) subsequently
nodes which are linked to multiple other nodes representing more
extracts the data from the new source, transforms the data in accor-
detailed information about each study. Also included in the Covid-
dance with the provided mapping information, and loads the data
Graph are case statistics and case data from Johns Hopkins Univer-
into the main CovidGraph.
sity [7] and population estimates from the United Nations World
Population Prospects (https://population.un.org/wpp/). Nodes in-
clude city, country, province, daily report and age group. Biomedi- 4 INTEGRATION OF SIMULATION STUDIES
cal data encodes information about genes, proteins, pathways and Via the aforementioned ETL-process, we connected the Covid-
different diseases associated with COVID-19. The data comprises Graph and the Management System for Models and Simulations
information from various biological and biomedical resources and (MaSyMoS, [8]). MaSyMoS is a Neo4j graph database for storing
is connected to Gene Ontology terms. The Gene Ontology is a re- and retrieving data items describing biomedical simulation studies.
source for computational representation of the function of genes The data is extracted from repositories for computational biology
and gene products [4]. Information about genes from the NCBI models (BioModels [15] and Physiome Model Repository2 [25])
Gene Database [2] is stored in Gene nodes which are connected and integrated in a single graph (Fig. 2 (B)). We consider a com-
to other nodes describing the underlying biology. Therefore, the putational biology model a mathematical model written in a for-
connected nodes include Gene Symbols according to the Ensembl mal machine-readable language, such that it can be systematically
Genome Browser, a genome database [10]. The gene symbols are parsed and employed by simulation and analysis software without
mapped to synonyms. Since genes are expressed in various tissues further human translation [12]. A biomedical simulation study is
the gene nodes are linked to Gtex Tissue nodes containing gene considered any calculation performed on a model and describing
expression data from the GTEx Portal [14]. For genes that are part evolution of the biological system represented, for instance, over
of a pathway there exists a relation between the corresponding spatial and/or temporal dimensions [23]. MaSyMoS links simulation
gene node and pathway node. The data included in the COVID- studies, their results and corresponding models. Curated simula-
Graph describes which genes are members of a pathway according tion studies are furthermore annotated with meta-data, primarily
2
(B)
(A)
Figure 2: (A) Original CovidGraph data model with data from i) Patents, ii) an index for biomedical terms (BioBERT [13]), iii)
BioMedical Ontologies [2, 4, 5, 9–11, 17, 21, 22], iv) COVID-19 related papers [3, 24], v) Clinical Trials [26]), vi) and a Statistical &
Geographic information [7, 16]. (B) A simplified MaSyMoS [8] meta graph containing i) simulation models formerly encoded
in SBML and CellML (not shown) [20], ii) simulation descriptions formerly encoded in SEDML [20], iii) bioontologies encoded
in OWL, iv) and links to publications in PubMed.
reference publications and ontological terms from bio-ontologies IDs (cmp. Figure 1). For Gene Ontology, ChEBI and Disease Ontol-
[4–6, 11]. MaSyMoS provides access to over 1000 manually cu- ogy more than 94% of the terms stored in MaSyMoS were connected
rated simulation studies originally published in BioModels. This set to terms in the CovidGraph. The UniProt coverage reached 41%.
contains highly curated studies targeting COVID-19 disease and
spreading (https://www.ebi.ac.uk/biomodels/covid-19). The result- Example: COVID-19 spread in Wuhan city. The simulation study
ing knowledge graph offers domain-specific retrieval and similarity by Roda at al. [19] investigates the COVID-19 spread in Wuhan
measures, and it enables efficient access and reuse. As all model city in the beginning of 2020. Figure 3 shows a Neo4j excerpt of the
have been shown to reproduce the published results, they are a model in MaSyMoS and the association to disease information in
valuable resource for biomedical investigations. the CovidGraph. The association is build by a matching reference
The integration of MaSyMoS data with CovidGraph was two- publication and a matching ontology entry from the Disease On-
folded: First we matched papers (publications) from both domains. tology. More specifically, the model is linked (in the middle, dark
Then we connected biomedical ontology terms from both resources green) to several resources (pink). For example, one annotation
thereby linking disease knowledge and biomedical simulation stud- refers to an ontology term from the Disease Ontology and is asso-
ies. The Paper data set (cmp. Fig. 2 (A)) in CovidGraph is represented ciated to the corresponding entry in the CovidGraph (on the right,
by different nodes (e.g., the abstract, authors, paper ID). In MaSyMoS brown). Another example is the reference publication which links
a paper is represented by a single publication node containing the to the corresponding publication in the CovidGraph (on the right,
same aforementioned set of information about a publication. Con- blue). We consider this example a first step towards bridging the
sequently, we mapped the corresponding IDs (PubMedID and DOI) gap between medical research and systems biology.
from CovidGraph paper ID nodes and MaSyMoS publication nodes,
thus connecting relevant publications from both data sets. This 5 TAKEAWAYS & FUTURE WORK
mapping resulted in 19 connections. This result is in our expected The CovidGraph project integrates COVID-related data from hetero-
range, as the underlying publication corpus covers different areas of geneous data sources, mainly from the medial and health domains,
interest (e.g. cell cycle, MAPK and apoptosis for simulation models into a single knowledge graph. We demonstrate that even for fairly
& clinical trials, respiratory studies and diseases for CovidGraph). distinct scientific domains such as computational biology modeling
The BioMedical data set in the CovidGraph represents different and clinical research, it is possible to link knowledge graphs and
ontologies with relevance for COVID-19 research. These ontologies thereby quickly provide new data sources. The presented version of
have possible connections and overlap with ontological terms used CovidGraph provides a tool set and a single-access point to previ-
to annotate simulation studies in MaSyMoS (cmp. Figure 2 (B)). Our ously disconnected data sources. Biomedical and clinician scientists
analyses showed that most overlap can be observed in gene infor- can explore a rich set of data items, which are not connected in any
mation, chemical entities, proteins and diseases. Consequently, we other resource. CovidGraph is only one example for rapid integra-
mapped ontological terms in MaSyMoS and CovidGraph for Gene tion of knowledge. The HealthECCO infrastructure offers solutions
Ontology (1810 connections), ChEBI (1211 connections), UniProt for integration and exploration of other diseases, building on the
(911 connections) and Disease Ontology (72 connections) by their same integration workflow showcased in this paper.
3
BIOMD…
Rate
mu Law for
MASYMOS_HAS_MODEL
Susce… http://id… http://id…
M MASYMOS_BELONGS_TO
Kausthu…
MA
MAS
N
O
TIO
_T
S
N…
YM
GS
rho
NC
YM
MA
_BBELO
OS
http://id…
S_is
TO
ON
FU
OR
OS_
MA
SY
MAS
S_
_H
S_
EL
SY M
AT
YMO
MO
NG
AS
AS
HA
MO
SS_
BELO
RE
YM
YM
O
S
S_
_P
M
S_
_H
YMOO
COVID-19
EL
AS
_C
OS MASYMOS_DOID_DESCRIBES_…
MAS
O
AR
AS
BE
http://id…
MAS YM
OS_
MA YM nOf
_B
YM
_h
_IS
_P
NG…
ersio
AM
SY
LO
as
OS
AR OS
AS
AS
S_isV
OS
MO Ta
is
AM
NG
_B YMO
ET
YM
M
S_ xo
YM
MASY ET EL n MAS
ER
MOS_BEL
S_
AS
beta ER TION O S_TO
AS
HAS_ MASYMOS_HAS_ANNOTA N…
TO
ONPA
M
Mon LONG
M
GSRAME S_BE
_T TER
Jul 13 YMO
MASY
O MAS
MOS_ n 19:19:55 MA
BELO Roda2020 xo CE… S
NGS_
TO s Ta YM
OS
- SIR model ha _B
S_
S_ha…
BE…
of COVID-… E
MA
SY O N… MA LO
NG
MO MASYMOS_BELONGS_TO YM LO S YM S _T
S_H AS BE
MOS_
OS O
S_ _is
N
AS
MO
N _C M De Why is it
CTIO
IO OM O scri
CT YM
MASY
PA bed difficult to
MASY
EA RT AS
TO
By
MA
_R ME accurately
REA
NT M
MA
MA
GS_
AS PAPER_HAS_PAPERID
SY
_H
SY
MO http://id… http://id… MASYMOS_RESOURCE_DESCRIBES_PAPERID 32289100 predict the
SY
HAS_
TO
MO
S S_B COVID-19
LON
O S_
MO
ELO
YM epidemi…
S
AS NG NG
_B
OS_
S
S_BE
LO S_ http://id…
_H MASYMOS_BEL
M TO
IES
EL
BE
M
AS M
MASYMOS_HAS_SPECI SYM
S_
YM
MA S_TO
ON
AS
EC
YMO
O
_R AS
YM
SY
YM Wuhan
MAS
G
_SP
AS
EA YM
O
_TO
MO A
S_
MAS
M
CT O
AS
BE
S_ SY
GS
M
IONS_
LO
S_H
BE M
MASY
ON
NG
LOO
O
S_
IN
EL
IES
IN IN
YM
Suscept… D_
HA
D_
NSG
TO
MASYMO
D_
Infected… TE
MOS_CO
S_B
_HS
ONGS_TO
CA TE
EC
S_
S
TE
CA
A_STO
_LO
MA
SP
SP
O
MA MA S_IS Infected… _LO
CA
_S
MA
S
YM
SY
MSO
EC
PE
IE
S_
MA
IS
LO
M
ES
SY YMO S_ MA EC
SY
OS
C
S
AIN
S_IS_LO
MA
IES
SY SP
NTAIN
MO
IE
IS_
_H
MA
S_
MA
MO
S_
M
S
AS
MA
HA Y
E…
S
IN OS_H
NT
AS
S_
_P S_ CIE
SY
OS
S_
RO PE TA
_…
SY
PR M
CO
_R
MO AS
MA OD S_S ON
S_SPEC
MO
DU MA
HA
_P
_H
MO
_IS
AS
SY IN
S_
CT SY _C RO
CATED_
TAUCT
SY
M MA
S_
AS
S_
MO N S DU
_H
OS S
OS
MO
O
S_
_C MO YM
MA
S_ CT
RE
_IS
IS_
_R
ISMOS
OS
SY OS
IS_
YM
_P SY
SY _PR MA
_IS
AC
E…
RE
RO
I…
YM
MA
S
RE
_P
MA
DU OD
IN
MA
RO
…
AC
S
CT UC
AC
DU
MA
T
TA
CT
…
…
Infected Confirm… Recover…
Suscept…
Figure 3: Simulation study by Roda at al. [19] represented in MaSyMoS (model in light blue) with links to CovidGraph.
The CovidGraph-Team hopes to motivate other data providers to Pedro Mendes, et al. 2005. Minimum information requested in the annotation of
link up with our resource, but we also like to discuss the applicability biochemical models (MIRIAM). Nature biotechnology 23, 12 (2005), 1509–1515.
[13] Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim,
of our graph database infrastructure on existing data silos. Chan Ho So, and Jaewoo Kang. 2020. BioBERT: a pre-trained biomedical language
representation model for biomedical text mining. Bioinformatics 36, 4 (2020),
1234–1240.
ACKNOWLEDGMENTS [14] John Lonsdale, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo,
The work presented here is the result of the HealthEcco Team (https: Saboor Shad, Richard Hasz, Gary Walters, Fernando Garcia, Nancy Young, et al.
2013. The genotype-tissue expression (GTEx) project. Nature genetics 45, 6 (2013),
//healthecco.org/team/). The COVID-19 collection in BioModels 580–585. https://doi.org/10.1038/ng.2653
was built with the help of an EOSC COVID-19 Fast Track funding. [15] Rahuman S Malik-Sheriff, Mihai Glont, Tung VN Nguyen, Krishna Tiwari,
Matthew G Roberts, Ashley Xavier, Manh T Vu, Jinghao Men, Matthieu Maire,
Sarubini Kananathan, et al. 2020. BioModels—15 years of sharing computational
REFERENCES models in life science. Nucleic acids research 48, D1 (2020), D407–D415.
[1] Renzo Angles and Claudio Gutierrez. 2008. Survey of graph database models. [16] United Nations. 2019. World population prospects 2019: highlights.
ACM Computing Surveys (CSUR) 40, 1 (2008), 1. [17] Kim D Pruitt, Tatiana Tatusova, and Donna R Maglott. 2007. NCBI reference
[2] Garth R Brown, Vichet Hem, Kenneth S Katz, Michael Ovetsky, Craig Wallin, sequences (RefSeq): a curated non-redundant sequence database of genomes,
Olga Ermolaeva, Igor Tolstoy, Tatiana Tatusova, Kim D Pruitt, Donna R Maglott, transcripts and proteins. Nucleic acids research 35, suppl_1 (2007), D61–D65.
et al. 2015. Gene: a gene-centered information resource at NCBI. Nucleic acids https://doi.org/10.1093/nar/gki025
research 43, D1 (2015), D36–D42. https://doi.org/10.1093/nar/gku1055 [18] Ian Robinson, Jim Webber, and Emil Eifrem. 2013. Graph Databases. O’Reilly
[3] Kathi Canese and Sarah Weis. 2013. PubMed: the bibliographic database. The Media, CA, USA.
NCBI Handbook 2 (2013), 1. [19] Weston C Roda, Marie B Varughese, Donglin Han, and Michael Y Li. 2020. Why
[4] The Gene Ontology Consortium. 2021. The Gene Ontology resource: enriching a is it difficult to accurately predict the COVID-19 epidemic? Infectious Disease
GOld mine. Nucleic Acids Research 49, D1 (2021), D325–D334. https://doi.org/10. Modelling 5 (2020), 271–281.
1093/nar/gkaa1113 [20] Falk Schreiber, Björn Sommer, Tobias Czauderna, Martin Golebiewski, Thomas E
[5] UniProt Consortium. 2019. UniProt: a worldwide hub of protein knowledge. Gorochowski, Michael Hucka, Sarah M Keating, Matthias König, Chris Myers,
Nucleic acids research 47, D1 (2019), D506–D515. https://doi.org/10.1093/nar/ David Nickerson, et al. 2020. Specifications of standards in systems and synthetic
gky1049 biology: status and developments in 2020. Journal of integrative bioinformatics
[6] Paula de Matos, Adriano Dekker, Marcus Ennis, Janna Hastings, Kenneth Haug, 17, 2-3 (2020).
Steve Turner, and Christoph Steinbeck. 2010. ChEBI: a chemistry ontology and [21] Lynn Marie Schriml, Cesar Arze, Suvarna Nadendla, Yu-Wei Wayne Chang,
database. Journal of cheminformatics 2, 1 (2010), 1–1. Mark Mazaitis, Victor Felix, Gang Feng, and Warren Alden Kibbe. 2012. Disease
[7] Ensheng Dong, Hongru Du, and Lauren Gardner. 2020. An interactive web-based Ontology: a backbone for disease semantic integration. Nucleic acids research 40,
dashboard to track COVID-19 in real time. The Lancet infectious diseases 20, 5 D1 (2012), D940–D946.
(2020), 533–534. https://doi.org/10.1016/S1473-3099(20)30120-1 [22] The GTEx Portal. 2020. GTEx Portal Documentation. https://gtexportal.org/
[8] Ron Henkel, Olaf Wolkenhauer, and Dagmar Waltemath. 2015. Combining home/documentationPage. Online, accessed 12 October 2020.
computational models, semantic annotations and simulation experiments in a [23] Dagmar Waltemath, Richard Adams, Daniel A Beard, Frank T Bergmann,
graph database. Database 2015 (2015), bau130. Upinder S Bhalla, Randall Britten, Vijayalakshmi Chelliah, Michael T Cooling,
[9] Daniel Scott Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Jonathan Cooper, Edmund J Crampin, et al. 2011. Minimum information about a
Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, and Sergio E simulation experiment (MIASE). PLoS computational biology 7, 4 (2011), e1001122.
Baranzini. 2017. Systematic integration of biomedical knowledge prioritizes drugs [24] Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang,
for repurposing. eLife 6 (Sept. 2017), e26726. https://doi.org/10.7554/elife.26726 Darrin Eide, Kathryn Funk, Rodney Kinney, Ziyang Liu, William Merrill, et al.
[10] Tim Hubbard, Daniel Barker, Ewan Birney, Graham Cameron, Yuan Chen, L 2020. Cord-19: The covid-19 open research dataset. ArXiv arXiv2004. (2020),
Clark, Tony Cox, J Cuff, Val Curwen, Thomas Down, et al. 2002. The Ensembl 10706v2.
genome database project. Nucleic acids research 30, 1 (2002), 38–41. https: [25] Tommy Yu, Catherine M Lloyd, David P Nickerson, Michael T Cooling, Andrew K
//doi.org/10.1093/nar/30.1.38 Miller, Alan Garny, Jonna R Terkildsen, James Lawson, Randall D Britten, Peter J
[11] Bijay Jassal, Lisa Matthews, Guilherme Viteri, Chuqiao Gong, Pascual Lorente, Hunter, et al. 2011. The physiome model repository 2. Bioinformatics 27, 5 (2011),
Antonio Fabregat, Konstantinos Sidiropoulos, Justin Cook, Marc Gillespie, Robin 743–744.
Haw, et al. 2020. The reactome pathway knowledgebase. Nucleic acids research [26] Deborah A Zarin, Tony Tse, Rebecca J Williams, Robert M Califf, and Nicholas C
48, D1 (2020), D498–D503. https://doi.org/10.1093/nar/gkz1031 Ide. 2011. The ClinicalTrials.gov results database - update and key issues. New
[12] Nicolas Le Novère, Andrew Finney, Michael Hucka, Upinder S Bhalla, Fabien England Journal of Medicine 364, 9 (2011), 852–860.
Campagne, Julio Collado-Vides, Edmund J Crampin, Matt Halstead, Edda Klipp,
4