# Information Mining for COVID-19 Research From a Large Volume of Scientific
Literature

Sabber Ahamed
Asurion
Nashville, TN 38152
<EMAIL_ADDRESS>
Manar D. Samad
Department of Computer Science
Tennessee State University
Nashville, TN 37209
<EMAIL_ADDRESS>

###### Abstract

The year 2020 has seen an unprecedented COVID-19 pandemic due to the outbreak
of a novel strain of coronavirus in 180 countries. In a desperate effort to
discover new drugs and vaccines for COVID-19, many scientists are working
around the clock. Their valuable time and effort may benefit from computer-
based mining of a large volume of health science literature that is a treasure
trove of information. In this paper, we have developed a graph-based model
using abstracts of 10,683 scientific articles to find key information on three
topics: transmission, drug types, and genome research related to coronavirus.
A subgraph is built for each of the three topics to extract more topic-focused
information. Within each subgraph, we use a betweenness centrality measurement
to rank order the importance of keywords related to drugs, diseases,
pathogens, hosts of pathogens, and biomolecules. The results reveal intriguing
information about antiviral drugs (Chloroquine, Amantadine, Dexamethasone),
pathogen-hosts (pigs, bats, macaque, cynomolgus), viral pathogens (zika,
dengue, malaria, and several viruses in the $coronaviridae$ virus family), and
proteins and therapeutic mechanisms (oligonucleotide, interferon,
glycoprotein) in connection with the core topic of coronavirus. The
categorical summary of these keywords and topics may be a useful reference to
expedite and recommend new and alternative directions for COVID-19 research.

_K_ eywords coronavirus $\cdot$ COVID-19 $\cdot$ text mining $\cdot$ graph
theory $\cdot$ topic modeling $\cdot$ betweenness centrality

##  1 Introduction

Severe acute respiratory syndrome (SARS) emerged in 2002 following the
outbreak of a new SARS-related coronavirus (SCV), which has resulted in
hundreds of deaths world-wide after infecting thousands of individuals
(Fouchier et al., 2003). Since then, there has been a plethora of research
aiming at understanding the etiology of the disease (Weiss and Navas-Martin,
2005), developing more reliable and faster diagnostic solutions (Yamaoka et
al., 2016), and innovating antiviral drugs (Keyaerts et al., 2009) and
vaccines (Yong et al., 2019). Many of these studies have successfully
repurposed previous findings to solve new challenges in biology and medicine.
For example, if a group of viruses shares a common protein structure, then
therapies for one viral infection can be repurposed for new diseases like
COVID-19. Unfortunately, viruses often mutate to several challenging strains
that are capable of infecting humans in a highly contagious manner. This
eventually triggers the onset of zoonotic infections, i.e., animal-to-human
and human-to-human transmission of the virus strain (Ozawa and Kawaoka, 2013).

The ongoing global pandemic of coronavirus disease (COVID-19) has emerged due
to a new strain of SCV named as the SARS coronavirus 2 (SARS-CoV-2) (Zhu et
al., 2020) that is arguably hosted by bats (Wu et al., 2020). As of March
31st, 2020, 857,487 people of 180 countries have been infected by the virus in
about three months, taking a toll of 42,107 human lives as the numbers
continue to grow exponentially (Dong et al., 2020). This outbreak has already
started to cause an unprecedented impact on humankind at a scale that
threatens to reshape the global economy, human lives, and the world beyond.
Unfortunately, it entails substantial time and effort to study these new
strains of viruses before developing a targeted and fail-safe drug or vaccine.
Without any immediate solution, several countries have recently approved the
use of hydroxychloroquine to treat critically ill patients diagnosed with
COVID-19 despite its known side-effects (Liu et al., 2020; Masuelli et al.,
2017). Notably, hydroxychloroquine is a drug for treating patients diagnosed
with malaria (Ben-Zvi et al., 2012). This repurposing of malaria drug to treat
coronavirus related patients suggests that topics of virology, genetic
engineering, bio-informatics, pharmacology in the literature may have valuable
recommendations for COVID-19 research.

Since the 2002 outbreak of coronavirus, many scholarly articles have been
published on the topics of SARS and SCV. It is not feasible for scientists to
survey such a massive volume of diverse literature to derive useful cross-
disciplinary insights within a short period of time. Computer-based modeling
of text documents can be a powerful approach in search of valuable information
from a large collection of diverse literature. In this paper, we have
developed a graph-based model using a large volume of scientific literature to
discover latent keywords that may recommend new information to combat the
battle against COVID-19.

##  2 Technical Background

In text analytics, keywords searching plays an important role in document
summarization (Hernández-castañeda et al., 2020), classification (Diaz-
Manriquez et al., 2018), and topic modeling (Jelodar et al., 2019). However,
the task of preserving semantic and syntactic information of a language in
numerical models is not trivial. There are three broad categories of text-to-
numeric representation models. First, term-frequency-inverse-document
frequency (TF-IDF) (Yun-tao et al., 2005), is one of the earliest methods for
text representation based on word count. However, such models do not capture
semantic relevance and syntactic ordering of the words. Second, manifold
learning techniques, such as skip gram (Cheng et al., 2006) and GLOVe
(Pennington et al., 2014), have been developed to efficiently capture semantic
relevance between words in numeric data vectors. These numeric embeddings do
not directly map the intricate relationship between words within a document.
Third, graphs can be used to develop an interconnected network of words of a
document to directly model the syntactic ordering and connectivity between
words. A graph-based keyword extraction model using collective node weight
(KECNW) has been proposed based on node-edge rank centrality measure (Biswas
et al., 2018). Several attributes, such as the distance from the central node,
the selectivity of each node based on a number of its connecting edges, and
the importance of neighboring nodes are taken into account to determine the
weight of each node. The node weights are used to rank individual words for
keyword selection. Additionally, graph-based models provide informative and
interactive visualization of complex interconnected network of words in a
document (Rusu et al., 2009). The goal of this paper is to develop a dense
graphical network of words to summarize thousands of research articles into
topics related to coronavirus. The collection of influential nodes or keywords
may provoke new research topics for the ongoing COVID-19 research.

##  3 Methodology

The steps involved in the proposed research methodology are discussed below.

###  3.1 Dataset

In this work, we use the publicly available COVID-19 Open Research Dataset
(CORD-19) released by a collaborative initiative of the Allen Institute for
AI, Chan Zuckerberg Initiative (CZI), Georgetown University’s Center for
Security and Emerging Technology (CSET), Microsoft, and the National Library
of Medicine (NLM) at the National Institutes of Health (NIH) at the request of
the White House Office of Science and Technology Policy. The dataset includes
about 13,000 scholarly articles with full texts in the areas of life science,
including pharmacology, immunology, physiology, microbiology, clinical
medicine, genetics, veterinary, and many more. The articles are related to the
study of COVID-19, SARS-CoV-2, and similar other coronaviruses. The dataset
comes with metadata and actual article texts in structured data fields that
identify title, paper and publication information, abstract, and the main body
texts. More details of the dataset can be found on the Kaggle website
(https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge),
which is a community-based data science platform for solving problems around
the world.

###  3.2 Proposed Graph Model

We propose a directed graph $G(N,E)$ based model that is represented by a set
of nodes $N$ and a set of edges $E$ with directions. Each node $n\in N$
represents a unique word. A directed edge $e\in E$ connects two adjacent words
based on their positioning in a sentence. The direction of an edge points from
the preceding word to the succeeding word in a sentence as shown in Figure 1.
Using this principle, a sequence-based text graph is first generated using
texts of the articles. In the next step, we determine the importance of each
node in the graph based on the concept of ’centrality’. In general, a node
with a higher centrality has more central positioning in the graph. For
example, the node with relatively shortest paths to all other nodes in the
graph has the highest measure of centrality (Hage and Harary, 1995). In this
paper, we measure the centrality of a node using the betweenness centrality
(BC) algorithm that is widely applied in social network analysis. However, the
BC algorithm has not been well studied in topic selection and modeling with a
large volume of texts, as we propose in this article. The BC algorithm is
originally proposed by Freeman (1977) and later upgraded by Brandes (2001) to
improve its computational performance.

Figure 1: Left: A pair of graph nodes with a directed edge between words w1
and w2. Right: A schematic diagram to illustrate the central $coronavirus$
node. The size of neighoboring nodes varies with BC measurements of their
corresponding words. Three influential nodes with high BC measurements and
their subgraphs are color coded.

In BC measurement, the node with highest centrality will be located in the
highest number of shortest paths between all other node pairs in the graph. In
other words, if a word frequently appears between the shortest paths of other
word pairs, then the word will have a high value of centrality. Thus, the BC
algorithm finds influential words that are imperative for understanding the
relationship between word pairs. Such transitive words are weighted as
representative nodes in the graph and selected as key topics for subsequent
analysis. Intuitively, this approach is more sophisticated and informative
than using a distance-based centrality measurement. If (u, v, w)$\in$ N such
that $u\neq v\neq w$, the shortest path between any two nodes can be
calculated using a standard Dijkstra’s algorithm. To measure BC of a node $w$
in the graph, the number of shortest paths between all other node pairs is
first obtained and denoted by $\sigma_{uv}$. Next, the number of shortest
paths ($\sigma_{uv}(w)$) is counted between all other node pairs that include
the target node $w$ on the path. The ratio of these two counting numbers is
calculated for each pair of nodes as the pair-dependency ($\delta_{u,v}$)
below.

$\displaystyle\delta_{u,v}=\frac{\sigma_{uv}(w)}{\sigma_{uv}}.$ (1)

The sum of pair-dependency across all possible pairs (excluding $w$) in the
graph yields the BC of the target node $w$ as below.

$\displaystyle BC(w)=\sum_{u\neq v\neq w}\frac{\sigma_{uv}(w)}{\sigma_{uv}}.$
(2)

Each node representing a unique word can be a representative topic by itself.
The corpus words are ranked by BC measurements to select semantically
important key topics with relatively high BC values. The key topics are then
used to create their corresponding subgraphs. Each topic word is the center of
the subgraph, and its neighboring words are the constituents of the topic. The
proposed method does not require the number of topics in advance compared to
other topic modeling algorithms such as latent Dirichlet allocation (LDA)
(Hoffman et al., 2010) and latent semantic analysis (LSA) (Landauer et al.,
1998).

###  3.3 Data Processing

We develop the proposed model and perform the text analysis on a Python
programming environment using several Python packages. These packages include
natural language tool kit (NLTK) for basic text analysis and regular
expressions package (re) for text preprocessing. A graph database Neo4J and a
Python to database connector library (py2neo) are used to build the graph. The
first step in text processing is to identify and remove ’stop words’ (words
with little or no merit as keywords) from the corpus. This step not only
reduces the complexity in the graph modeling but also puts more weights on
relevant candidate words in the graph. The dataset includes research articles
composed in Chinese, Russian, Arabic, and Korean languages. We confine our
analysis to English articles by excluding all non-English words using regular
expression (regex). Regex is also used to filter out undesired symbols and
numeric data. The graph data, including nodes and edges, are exported to Gephi
(Bastian et al., 2009) for better visualization and manipulation of the graph
models and model-driven results. All the notebooks with source code and data
are publicly shared in a Github project repository
(https://github.com/msahamed/covid19-text-network).

###  3.4 Modeling and Analysis

The proposed graph model is developed using all available article abstracts in
the dataset. We assume that an abstract has succinct and sufficient
information to represent the overall content of a research article. Once the
global graph is developed using the article abstracts, we find several
influential words based on their BC values that are linked to $coronavirus$.
Next, we build separate subgraph for each of the selected key topic as shown
in Figure 1. Within each subgraph, neighboring nodes of a key topic are
expected to reveal a variety of information about the topic word. The
neighboring words are further grouped into several categories. Within each
category, we rank order the neighboring words using BC values obtained from
the subgraph model to identify their relative importance. We discuss our
findings of this approach in the next section.

##  4 Results and Discussion

The CORD-19 dataset has a substantial number of articles with missing
abstracts. Therefore, we use available abstracts for 10,683 research articles
to build our proposed graph model. In the data processing step, we have
created a list of 2796 stop words based on the word frequency and visual
scrutiny to exclude them from the corpus. We first find the word $coronavirus$
in the global graph to select its three neighboring nodes with high BC values.
Since BC values can be exponentially very large, we take natural logarithm of
these values (log BC). We select three key topics: $transmission$, $drug$, and
$gene$ with log BC values of 16.65, 16.75, and 18.27, respectively. In the
following subsections, we discuss our findings on the three subgraph models
corresponding to $transmission$, $drug$, and $gene$ topics, respectively.

Table 1: Neighboring words in the $transmission$ subgraph linked to $coronavirus$. Topic |  Connected nodes in the $transmission$ subgraph
---|---
Host |  ’Rats’, ’Livestock’, ’Mammals’, ’Goats’, ’Camels’, ’Mosquito’, ’Pigs’, ’Monkeys’, ’Dogs’, ’Poultry’, ’Rodents’, ’Horses’, ’Bird’, ’Chimpanzee’, ’Honeybees’, ’Mites’, ’Puma’, ’Snails’, ’Infants’, ’Ants’, ’Flies’ , Rodents, Drosophila
Disease |  ’Malaria’, ’Rabies’, ’Dengue’, ’Chikungunya’, ’Measles’, ’Schistosomiasis’, ’Monkeypox’, ’Poliovirus’, ’Cholera’, ’Lyssavirus’, ’Tuberculosis’, ’Rhinovirus’, ’Adenovirus’, ’HFMD’, ’HIV’, ’Paramyxovirus’, ’Zikavirus’, ’Fungal’, ’Leishmania’, ’Myocarditis’, ’Venereal’
Miscellaneous |  ’Distance’, ’Fever’, ’Traveler’, ’Contacts’, ’Exposures’, ’Humidity’, ’Infants’, ’Pregnancy’, ’Interspecies’, ’Mucosal’, ’Reticulum’, ’Zoonosis’, ’Sanitization’, ’Bioaerosols’, ’Aerobiology’, ’Tropisms’, ’Human-to-human’, ’Transspecies’, ’Touches’, ’MDRO’, ’Fluoroquinolone’.

###  4.1 Transmission Subgraph

Studies related to infectious disease transmission are important in
understanding the spread and growth of pathogens in different media. Table 1
shows the neighboring words of the $transmission$ subgraph. The subgraph
reveals a number of host-pathogen interactions. Animal hosts such as poultry,
camel, dogs, pigs, monkeys, chimpanzees may be candidates for studying the
transmission mechanism of different zoonotic pathogens related coronavirus.
The coronavirus related topic $transmission$ also connects to a number of
diseases and pathogens, including rabies, dengue, tuberculosis, adenovirus,
malaria, measles, and hand-foot-and-mouth disease (HFMD). These pathogens may
have interesting traits in common with the transmission of coronavirus.
Keywords such as multi-drug resistant organism (MDRO), tropism (the growth or
movement of organisms), zoonosis (the topic of disease transmission from
animals to humans), sanitization, bioaerosols have expectedly appeared in the
subgraph of transmission topic. Additionally, the subgraph includes words
related to different transmission media such as blood, respiratory, lung,
water, aerosol, oronasal, gut, mouth, trachea, glands, droplet, saliva,
airborne, and semen. Other miscellaneous keywords are presented in Table 1
that includes infants and pregnancy that are important topics for transmission
studies.

Figure 2: Rank ordering of the most important keywords in the transmission
subgraph that is originally linked to the coronavirus node. The subgraph
keywords are grouped into (a) host and (b) disease categories. Log of BC is
the natural logarithm (log) of betweenness centrality.

Figure 2 shows the most important keywords in the $transmission$ subgraph
based on their BC measurements. Among the pathogen hosts, pigs, dogs, and
camels have topped the list. Although bats do not appear in this list of
hosts, they do appear later in the gene subgraph. The top diseases in the rank
ordering, such as dengue, rabies, malaria, adenovirus, may have some topics
common with the study of coronavirus transmission. The rhinovirus related to
common cold and lyssavirus often hosted by bats have appeared in the ranking.
The adenovirus, ranked in the third position, causes symptoms similar to those
of coronavirus infection.

Table 2: Neighboring words in the $drug$ subgraph linked to $coronavirus$. Topic |  Connected nodes in the $drug$ subgraph
---|---
Drug |  ’Oseltamivir’, ’Amantadine’, ’Viagra’, ’Tunicamycin’, ’Aspirin’, ’Paracetamol’, ,’Chloroquine’, ’Meropenem’,’Troglitazone’, ’Bleomycin’, ’Dexamethasone’, ’Antimoniate’, ’Nitazoxanide’, ’Niclosamide’, ’Resveratrol’, ’Gemcitabine’, ’Rivastigmine’, ’Amphotericin’, ’Paromomycin’, ’Miltefosine’, ’Sitamaquine’, ’Berenil’, ’Daunorubicin’, ’Artemether’, ’Buprenorphine’, ’Acetaminophen’, ’Verdinexor’, ’Mizoribine’, ’Amodiaquine’, ’Sunitinib’, ’Carbapenem’, ’Amiodarone’, ’Boceprevir’, ’Pranobex’, ’Dapsone’, ’Betalactam’, ’Tanespimycin’, ’Saracatinib’, ’Vildagliptin’, ’Ciclosporin’, ’Cyclosporin’
Chemical |  ’Glycoprotein’, ’Oligonucleotides’, ’Glycosylation’, ’Cyclophilins’, ’Glucocorticoid’, ’Acetylcholinesterase’, ’Estrogen’, ’Cyclooxygenase’, ’Glycyrrhizin’, ’Hexachlorophene’, ’Myricetin’, ’Glycosides’, ’Hexamethylene’, ’Phytomedicines’, ’Phytoconstituents’, ’Farnesyltransferase’, ’Imbricataloic’, ’Salicylanilide’, ’Quinoxalines’, ’Amidohydrolases’, ’Enantiomers’, ’Ectoenzyme’,’Isoleucin’, ’Hydrochloride’, ’Calcium’, Glutamate, Phytochemicals’, ’Diphosphate’, ’Phisohex’, ’Monoammonium’
Disease |  ’HIV’, ’Pneumonia’, ’Flavivirus’, ’Chikungunya’, ’Zikavirus’, ’Monkeypox’, ’Melanoma’,’Dengue’,’Tuberculosis’, ’BTCOV’, ’Hypertension’, ’Phospholipidosis’, ’Hematemesis’, ’Acinetobacter’, ’Baumannii’
Miscellaneous |  ’Metabolism’, ’Pharmacokinetics’, ’Seroprevalence’, ’Bactericidal’, ’Antifungals’, ’Macropinocytosis’, ’Toxoplasmosis’, ’Syncytia’, ’Pyrenees’, ’Decoction’,’Nutraceuticals’, ’Chemoprevention’, ’Clerodendrum’ , ’Molluscicide’, ’Myelosuppression’, ’Prophylaxis’, ’Macrocyclization’,
Figure 3: Rank ordering of the most influential keywords in the $drugs$
subgraph that is linked to the $coronavirus$ node. The keywords of the
subgraph are then grouped into drugs, chemical compounds, diseases, and
miscellaneous categories. Log of BC is the natural logarithm (log) of
betweenness centrality.

###  4.2 Drug Subgraph

The study of drugs (pharmacology) plays a big role in the battle against
diseases. Table 2 shows the connected words of the $drug$ subgraph that is
linked to the $coronavirus$ node. A long list of drugs have appeared in the
literature, including Chloroquine. Several antiviral drugs such as
’Verdinexor’, ’Oseltamivir’, ’Amantadine’, ’Pranobex’ have appeared in the
$drug$ subgraph. Other important drugs that appear in the subgraph are drugs
for cancer treatment (Gemcitabine, Berenil, Daunorubicin, Tanespimycin), anti-
fungal drug (Amphotericin), antibiotics (Paromomycin, Carbapenem, Betalactam),
immunosuppressive drugs (Mizoribine, Cyclosporin), drugs for malaria
(Amodiaquine) and Hepatitis C virus treatment (boceprevir). Many chemical and
organic compounds, proteins and enzymes are mentioned in the literature that
may have important pharmaceutical properties. Plants (Clerodendrum), plant-
based organic compounds (Resveratrol, Glycyrrhizin), and medicine derived from
plants (Phytomedicines, Phytochemicals, Phytoconstituents) have appeared in
the literature. Bat coronavirus (BTCOV) appears on the list as well.
Interestingly, ’Chikungunya’, ’Monkeypox’, ’Dengue’, and ’Tuberculosis’
appeared both in the subgraphs of $drug$ and $transmission$. The pathogens of
these diseases may have useful links to the study of drug and transmission of
coronavirus.

We group the neighboring words within the $drug$ subgraph into four
categories: drug, chemical compound, disease, and miscellaneous. Figure 3
shows the most important keywords under four different categories.
Interestingly, antiviral drug $Amantadine$ has the highest rank even before
$Chloroquine$. $Amantadine$ is administered to treat symptoms related to flu
viruses although it may not be effective for treating illness due to all types
of virus strains. $Dexamethasone$ has almost similar BC measurement as
$Chloroquine$, which is a drug to aid the immune system in the fight against
inflammation. In the disease category of the $drug$ subgraph, zikavirus and
pneumonia appear as the top two keywords followed by HIV and dengue. This rank
ordering is quite different from that of the $transmission$ subgraph (Figure
2) where dengue topped the list. There may be common pharmaceutical interests
shared by the drugs used for zikavirus and coronavirus treatment.

In the chemical category, Glycoprotein and a chemical reaction known as
Glycosylation have topped the list. Oligonucleotides, single strands of RNA,
have ranked fourth in this category presumably because of their known effect
in inhibiting the replication of influenza virus (Kumar et al., 2013).
Interestingly, plant-based compound Glycyrrhizin ranks fifth in the list,
which has been used to cure bronchitis, gastritis, and jaundice. There are
other proteins and enzymes in the list that will require further discussion by
the field experts. The miscellaneous category suggests research studies
focusing on ’metabolism’, ’prophylaxis’ (actions to prevent disease),
’pharmacokinetics’ (movement of drugs in the body), and ’seroprevalence’
(number of individuals tested positive based on blood serum).

###  4.3 Gene Subgraph

The study of genes is imperative as it distinguishes different virus strains
and identifies target proteins for successful discovery of drugs and vaccines.
The $gene$ subgraph includes a large number of diseases and pathogens as shown
in Table 3. The list includes Hepatitis viruses (HCV, HBV, HEV), influenza
viruses (H1N1, H5N1, H7N9, PR8), other viruses related to Dengue (Denv),
Chikungunya (CHIKV), HIV, zikavirus, and malaria. Many of these viruses can
share similar genomic structure and replication strategy that can be
repurposed in the pathogenic study and drug discovery of novel virus strains
such as COV-2. Coronaviruses related to pigs, such as porcine epidemic
diarrhea virus (PEDV), porcine hemagglutinating encephalomyelitis virus
(PHEV), mouse hepatitis virus (MHV), feline infectious peritonitis (FIP)
virus, mice originated human coronavirus (HKU1), alpha- and Betacoronaviruses
(mainly infect bats) appear in the subgraph as they belong to the same
$coronaviridae$ virus family. In the host list, several species of monkey
(Macaque and Cynomolgus) have appeared in addition to pigs and bats. The
result is a proof-of-concept that bats are subjects of genetic research
related to coronavirus. The discovery of coronavirus strains in hosts like
pangolin is a very recent discovery and is still under active research (Lam et
al., 2020).

Table 3: Neighboring words in the $gene$ subgraph linked to $coronavirus$. Topic |  Connected nodes in the $gene$ subgraph
---|---
Disease |  ’Cancer’, ’Zikavirus’, ’HCV’, ’RSV’, ’Pneumonia’, ’Influenza’,’SARS’, ’PEDV’, ’Dengue’, ’HIV’, ’IBV’, Adenovirus’, ’Rabies’, ’Hepatitis’, ’PRRSV’, ’H1N1’, ’H5N1’, ’CHIKV’, ’Rhesus’, ’HCOV’, ’ARDS’, ’Denv’, ’Flavivirus’, ’HRV’, ’Rhinovirus’, ’Malaria’, ’Rotavirus’, ’HMPV’, ’Tuberculosis’, ’Enterovirus’, ’HBV’,’HEV’,’COPD’, ’Asthma’, ’Ebolavirus’, ’Herpesvirus’ ,’Measles’, ’MHV’, ’Cytomegalovirus’,’Sepsis’, ’Astrovirus’, ’Bronchitis’, ’FIP’, ’Alphavirus’, ’EVB’, ’H7N9’, ’PVC2’, ’CMV’, ’Paramyxovirus’, ’Betacoronavirus’, ’Reovirus’, ’VSV’, ’Polyomavirus’, ’Atherosclerosis’,’HSV’, ’BRSV’, ’KSHV’, ’CPV’, ’Torovirus’, ’HKU1’, ’Circovirus’, ’BRD’, ’GBS’, ’RRV’, ’Cirrhosis’, ’Orthoreovirus’,’Hypoxia’, ’Bunyavirus’, ’PHEV’,’Hartmanivirus’,’PVX’, ’Dyskinesia’, ’Lassa’, ’PR8’, ’VWD’, ’HPYV6’
Host |  ’Macaque’, ’Pigs’, ’Cats’,’Bat’, ’Mice’, ’Swine’, ’Poultry’, ’Birds’, ’Porcine’, ’Cattle’, ’Camels’, ’Cynomolgus’, ’Horses’, ’Ades’
Biomolecular |  ’PCR’, ’RNA’, ’DNA’, ’ACE2’, ’NGS’, ’Aptamers’, ’Metagenomics’, ’QPCR’, ’SIRNA’, ’CD8’, ’Ceacam1’, ’Haplotypes’, ’MIRNA’,’DPP4’, ’KDA’, ’STAT3’, ’APOD’,’IFNAR’, ’SHRNAS, ’APOE’, ’vsiRNAs’, ’APOM’, ’IFN’, ’Glycoprotein’, ’Corticosteroids’,’PRP’, ’Macrolide’, ’Macrophages’, ’Cytokine’, ’Interferon’, ’Nucleotide’, ’Polymerase’, ’Demyelination’, ’Immunoglobulin’, ’Astrocytes’, ’Chemokines’, ’Myelin’,’PRF’,’TH2’, ’Kinases’, ’Pleiotropic’, ’ORF3’, ’Phosphoprotein’, ’Kegg’, ’Caspase’, ’Peptidase’, ’Eukaryotes’, ’Mycoplasma’, ’Cryptosporidium’, ’Mycobacterium’, ’MBL2’
Figure 4: Rank ordering of the most influential keywords in the $gene$
subgraph linked to the $coronavirus$ node. The subgraph keywords are grouped
into disease, host, biomolecular, and miscellaneous categories. Log of BC is
the natural logarithm of betweenness centrality.

We further group the neighboring words of the $gene$ subgraph into four
categories: host, disease, bio-molecular, and miscellaneous. The biomolecular
category includes proteins, cellular mechanisms, drugs, and biochemical
compounds. Figure 4 shows the rank ordering of keywords of each category based
on BC measurements. Interestingly, cancer appears after SARS, influenza,
pneumonia, which suggests a link between the genomic studies of cancer and
virus. The porcine epidemic diarrhea virus (PEDV) appears in the fifth
position suggesting an important pathogen in the genetic study of flu viruses.
In the bimolecular category, polymerase chain reaction (PCR), a mechanism used
in molecular biology for testing coronavirus, has ranked second after RNA. The
abbreviation of interferon (IFN) appears fourth in this category and second in
the miscellaneous keyword category, which is a set of proteins released by the
host in response to a virus attack. In the host category, mice rank the top
followed by bat, cat, pig, and a species of monkey (macaque). There are other
potentially intriguing keywords in the ranking that will require more space
and scope for further discussion.

###  4.4 Summary and Limitations

We have developed a graph-based model to mine information for COVID-19
research. The major findings are as follows. 1) The proposed graph model is
effective in searching informative and connected keywords from a large volume
of documents. The BC measurement of individual words can rank their importance
that has shown intuitive and informative results. 2) pigs appear in both
$transmission$ and $gene$ subgraphs as an important host of pathogens related
to the $coronaviridae$ virus family (PEDV and PHEV), 3) in addition to dengue
and malaria, zikavirus may have a connection with the development of drugs for
flu viruses, 4) our model finds a potential link between genome research of
cancer and flu virus, 5) a list of antiviral drugs have been identified where
’Amantadine’ ranks above ’Chloroquine’ in the treatment of flu virus
infection. More importantly, the authors of a recent review on finding
therapies for COVID-19 (Li and De Clercq, 2020) highlight the importance of a)
repurposing antiviral agents used in treating HIV, HBV, HCV, and Influenza, b)
oligonucleotide and interferon-based therapies, c) glycoprotein as a target
for an antiviral agent, d) reference to RNA viruses (Influenza, Ebola,
chikungunya, and enterovirus), e) a drug ’Nitazoxanide’ to inhibit COV-2 - all
of which appear in our subgraph models and rank order. We believe similar
other keywords will intrigue the scientists to find repurposing candidates for
COVID-19 research.

Despite promising findings, there are several limitations of this study.
First, high BC values of top ranked keywords may be partly attributed to their
high prevalence in the literature. There is a possibility that keywords in the
lower rank order are less prevalent in the literature. However, low-ranked
keywords can still be strong candidates for future COVID-19 research. Second,
our analysis is limited to article abstracts without incorporating full
article texts that can be a richer source of information. Third, we do not use
any explicit metric to evaluate our graph model performance and findings since
this study is entirely exploratory. Fourth, our graph model includes a broad
array of diverse health science literature. A grouping of research articles
and subsequent analysis within individual subgroups may yield more focused
results. We leave these tasks for our future work.

##  5 Conclusions

This paper demonstrates one of the first studies on mining a large volume of
health science literature in search of information for the ongoing battle
against COVID-19. The results presented in tables and figures under different
topics effectively summarize the key information contents of the literature
with the aid of a graph-based computational model and betweenness centrality
based keyword ranking. The proposed model has been able to recommend
informative drug types, pathogens, hosts, chemical and biomolecules for
COVID-19 research that may not always be evident to the concerned researchers.
The findings may be a useful reference guide to provoke new ideas and expedite
new directions of research in the fight against the COVID-19 pandemic.

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