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SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping

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Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.
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Nucleic Acids Research, 2018 1
doi: 10.1093/nar/gky1021
SymMap: an integrative database of traditional
Chinese medicine enhanced by symptom mapping
Yang Wu1,2,, Feilong Zhang1,, Kuo Yang 3,, Shuangsang Fang2, Dechao Bu2, Hui Li2,
Liang Sun2,HairuoHu
2,KuoGao
1, Wei Wang1, Xuezhong Zhou3,*, Yi Zhao1,2,* and
Jianxin Chen1,*
1Beijing University of Chinese Medicine, ChaoYang District, Beijing 100029, China, 2Key Laboratory of Intelligent
Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese
Academy of Sciences, Beijing 100190, China and 3School of Computer and Information Technology and Beijing Key
Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
Received August 15, 2018; Revised September 25, 2018; Editorial Decision October 11, 2018; Accepted October 22, 2018
ABSTRACT
Recently, the pharmaceutical industry has heav-
ily emphasized phenotypic drug discovery (PDD),
which relies primarily on knowledge about pheno-
type changes associated with diseases. Traditional
Chinese medicine (TCM) provides a massive amount
of information on natural products and the clinical
symptoms they are used to treat, which are the ob-
servable disease phenotypes that are crucial for clin-
ical diagnosis and treatment. Curating knowledge
of TCM symptoms and their relationships to herbs
and diseases will provide both candidate leads and
screening directions for evidence-based PDD pro-
grams. Therefore, we present SymMap, an integrative
database of traditional Chinese medicine enhanced
by symptom mapping. We manually curated 1717
TCM symptoms and related them to 499 herbs and
961 symptoms used in modern medicine based on
a committee of 17 leading experts practicing TCM.
Next, we collected 5235 diseases associated with
these symptoms, 19 595 herbal constituents (ingre-
dients) and 4302 target genes, and built a large het-
erogeneous network containing all of these compo-
nents. Thus, SymMap integrates TCM with modern
medicine in common aspects at both the phenotypic
and molecular levels. Furthermore, we inferred all
pairwise relationships among SymMap components
using statistical tests to give pharmaceutical scien-
tists the ability to rank and filter promising results
to guide drug discovery. The SymMap database can
be accessed at http://www.symmap.org/ and https:
//www.bioinfo.org/symmap.
INTRODUCTION
Two main approaches are used in modern drug discov-
ery: target-based drug discovery (TDD) and phenotypic
drug discovery (PDD) (1). TDD begins with a well-dened
molecular target for a specic disease, and compound li-
braries are generated from which optimal compounds with
activity against the target are identied. In contrast, PDD
does not rely on knowledge of molecular targets, but is
rather based on screening a large number of compounds
and monitoring phenotypic changes. An inuential analysis
in 2011 reported that PDD has been more productive than
TDD as a means of discovering rst-in-class drugs (2).
Isolation and further derivatization of natural products
from traditional medicines is a promising PDD strategy (3).
The traditional use of natural products has been extensively
documented in diverse cultures for millennia, and these de-
scriptions provide valuable therapeutics drug leads for spe-
cic disease phenotypes. It is shown that, of 122 traditional
medicine-derived compounds used as drugs in countries
hosting WHO-Traditional Medicine Centers, 80% were
used for their traditional purpose or a related ethnomedical
purpose (4). These ndings demonstrate the value of tra-
ditional medicinal knowledge in the quest to discover new
biologically active compounds.
Traditional Chinese medicine (TCM) provides a massive
amount of information on natural products and the clini-
cal symptoms they are used to treat (5), which are the ob-
servable disease phenotypes that are crucial for clinical di-
agnosis and treatment (6). These empirical knowledge can
shed light on PDD screening directions in modern drug dis-
covery. For example, the discovery of ephedrine, an anti-
*To whom correspondence should be addressed. Tel: +86 10 6260 0822; Fax: +86 10 6260 1356; Email: biozy@ict.ac.cn
Correspondence may also be addressed to Xuezhong Zhou. Tel: +86 10 5168 4931; Fax: +86 10 5168 4931; Email: xzzhou@bjtu.edu.cn
Correspondence may also be addressed to Jianxin Chen. Tel: +86 10 6428 6398; Fax: +86 10 6428 6398; Email: cjx@bucm.edu.cn
The authors wish it to be known that, in their opinion, the rst three authors should be regarded as joint rst authors.
C
The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
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2Nucleic Acids Research, 2018
asthmatic drug identied by the rst TCM pharmacologist
Kehui Chen (1898–1988), was inspired by the clinical use of
the Chinese herb Ma Huang to treat asthma for >4000 years
(7). Artemisinin (qinghaosu), the rst-line drug for malaria,
was discovered by 2015 Nobel laureate Youyou Tu, who
was inspired by the Chinese herb qinghao for combating the
symptoms of malaria in TCM (8). Consequently, standard-
ization of the symptom vocabulary of TCM and further il-
lustration of the relationships between symptoms, natural
products (mainly herbs), diseases, and molecular targets has
the potential to provide novel lead/drug candidates.
Knowledge of the symptoms traditionally treated by
herbs is difcult for modern pharmaceutical scientists to
understand for two reasons. Firstly, most TCM symptom
terms are written in ancient Chinese. However, only a tiny
fraction of Chinese intellectuals alive today can understand
the denitions of TCM symptoms exactly. Secondly, to bet-
ter leverage the knowledge of TCM usage, TCM symptoms
must be mapped onto the terms for symptoms used in mod-
ern medicine (MM). As TCM is based on a holistic philos-
ophy that differs substantially from that of MM (9), this
task can be accomplished only by experts who are trained
in TCM and familiar with MM. However, the number of in-
dividuals qualied to perform this task has declined contin-
uously in recent years. Accordingly, linking TCM symptom-
herb relationships to MM, as well as the molecular mecha-
nisms underlying diseases, is urgent.
Therefore, we built a new database, SymMap, an integra-
tive database of traditional Chinese medicine enhanced by
symptom mapping. During the development of SymMap,
the difculties mentioned above were overcome by form-
ing a committee of 17 leading experts practicing TCM.
SymMap provides four types of new knowledge. Firstly, we
manually standardized TCM symptom terms and deni-
tions, which were mapped to herbs registered in the Chinese
Pharmacopoeia, the collection of TCM knowledge with
very high level of evidence. Secondly, we rigorously mapped
these TCM symptoms to MM symptoms recorded in the
unied medical language system (UMLS) via expert con-
sensus and subsequent manual verication. Thirdly, using
database mining, we mapped the knowledge of symptom-
herb relationships onto current data regarding the molecu-
lar mechanisms of TCM, including the compound composi-
tions of herbs (ingredients), their molecular targets (mainly
genes/proteins), and diseases related to symptoms or tar-
gets. Finally, we present all-versus-all pairwise associations
among all components in SymMap, with some of them an-
alyzed by statistical inference to enable pharmaceutical sci-
entists to rank and lter the most promising results.
In the last decade, several databases focusing on different
aspects of TCM knowledge have been published. For exam-
ple, the TCM-ID (10), HIT (11), TCMID (12), and TCMSP
(13) databases. These databases have undergone continu-
ous stepwise improvement as new components or aspects
of TCM have been added. However, information regarding
symptoms and phenotypes has never been curated, stan-
dardized, and connected to herbs and diseases, as well their
underlying molecular mechanisms. Thus, SymMap lls the
gap and presents the newly curated symptom-herb knowl-
edge, which can provide both pharmacological effects (phe-
notypic changes) and candidate leads for PDD screening ef-
forts. In addition, SymMap provides symptom-mechanism
mapping that will enable further analysis of the shared
symptoms and targets of multiple diseases for accelerating
drug repositioning studies.
DATA COLLECTION AND PROCESSING
Data sources of SymMap
SymMap contains six components: symptoms used in TCM
(TCM symptoms) and MM (MM symptoms), herbs, ingre-
dients, targets (also denoted as genes in the article) and dis-
eases (Figure 1A). Among these components, TCM symp-
toms, MM symptoms and herbs can be regarded as the phe-
notypic knowledge that are valuable for PDD programs,
whereas the ingredients, targets, and diseases consist of
molecular information derived from TDD efforts.
We introduced phenotype-level information by extract-
ing TCM symptoms and herb terms from the Chinese Phar-
macopoeia (CHPH, 2015 edition). A description about the
elds of each record in CHPH is illustrated in Supplemen-
tary Figure S1. We rstly invited 17 leading experts practic-
ing TCM (Supplementary Table S1) to manually check all
symptom terms from the CHPH (Figure 1B). The names
of TCM symptoms were standardized according to an au-
thoritative TCM publication, ‘Standardization research on
TCM Terminology’ (Zhongyiyao Mingci Shuyu Guifanhua
Yanjiu, published in 2016), and a published platform for in-
tegrating TCM terminologies (14). And we curated the de-
nition, locus, and property information for these symptoms
using another TCM publication, ‘Standardization of Patho-
logical Terminology’ (Bingzhuang Shuyu Guifanhua Jichu,
published in 2015). Then, we collected MM symptom terms
from the MeSH (version 2017) (15), SIDER (version 2017)
(16) and UMLS (version 2016) (17) databases, after which
the expert committee manually mapped TCM symptoms to
MM symptoms.
Next, we collected molecular information mainly by
database integration. For example, we integrated the in-
gredient information from the TCMID (version 2015),
TCMSP (version 2.3) and TCM-ID (version 1.0) databases.
To obtain non-redundant ingredient records for herbs,
we select from different records with common identi-
er provided by the three databases, such as CAS, Pub-
Chem CID and InChI Key etc. The target component of
the database was collected from two sources: the target
genes of TCM compounds from the HIT (version 2.0) and
TCMSP databases, and the target genes of modern dis-
eases from the HPO (version 2017) (18), DrugBank (version
5.0.0) (19) and NCBI gene (version 2018) (20)databases.
Similarly, the disease component was also merged from two
sources, namely OMIM (version 2017) (21) and Orphanet
(version 2017) (22) databases. The sources of all compo-
nents of the SymMap database are summarized in Table 1.
Direct associations among SymMap components
There are six direct associations among the components
(Figure 1A). Two relationships, the TCM symptom-herb
and TCM symptom-MM symptom associations, had never
before been included in a public database, but they were
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Nucleic Acids Research, 2018 3
Figure 1. Schematic of the SymMap database. (A) Upper panel: the six components contained in SymMap are illustrated in six circles in different colors in
the middle. The blue arcs connecting the circles show the six direct associations, with the numbers of associations shown at the left. The gray dotted lines
connecting the six components show the nine indirect associations, with the numbers of associations shown in the right. Lower panel: implementation of
the functions of SymMap. (B) Illustration of the scheme for extraction, curation and standardization of TCM symptom terms and their relationships to
herbs. (C) Illustration of the scheme for expert curation of TCM symptom-MM symptom mapping.
Table 1. Overview of the data curated in SymMap
Components Data source Amount
Herbs Extracted from the Chinese pharmacopoeia
(2015 edition)
499
TCM symptoms Extracted, manually curated, and
standardized from the Chinese
pharmacopoeia (2015 edition)
1717
MM symptoms Indexed in the UMLS database, and
manually mapped to TCM symptoms
961
Ingredients Integrated from the TCMID, TCMSP and
TCM-ID databases
19 595
Targets Integrated from the HIT, TCMSP, HPO,
DrugBank and NCBI databases
4302
Diseases Integrated from the OMIM, MeSH and
Orphanet databases
5235
included in SymMap for the rst time as a result of man-
ual curation by experts. Other relationships, including the
MM symptom–disease, herb–ingredient, ingredient–target
and target–disease (also referred to as gene–disease) associ-
ations, are dispersed distributed in multiple databases that
required integration.
We rstly mapped two types of direct associations to
TCM symptoms by manual curation. The TCM symptom–
herb relationships were obtained directly from the CHPH
after standardization of TCM symptom terms (Figure 1B).
TCM symptom-MM symptom mapping was conducted us-
ing an iterative process. For each TCM symptom term, three
experts were randomly selected and given a full list of MM
symptom terms. If the experts did not map the TCM term
to the same MM symptom then another expert was as-
signed, and this process was repeated until at least two ex-
perts reached an agreement. After all TCM symptoms were
mapped, manual rechecking was conducted to ensure the
accuracy of the database (Figure 1C). Note that the full list
of MM symptoms in UMLS identiers contains not only
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4Nucleic Acids Research, 2018
concepts about symptoms, but also other types of terms in
modern medicine (Supplementary Table S2).
Next, we curated additional direct associations by
database integration. The MM symptom–disease relation-
ships were aligned and connected based on the HPO,
OMIM and Orphanet databases. We mapped the UMLS
ids of MM symptoms into the HPO ids rst, and then
related the HPO identiers of symptoms to the disease
terms in OMIM or Orphanet identiers based on the HPO
records. It is noteworthy that a number of diseases have both
OMIM and Orphanet identiers. In this case, we merged
the disease terms according to their names in a case insen-
sitive way. The herb–ingredient associations were merged
from the TCMSP, TCMID, and TCM-ID databases. The
ingredient–target associations were obtained from the HIT
and TCMSP databases, whereas the gene–disease associa-
tions were aligned and obtained from the HPO and OMIM
databases. For database integration, we carefully checked
the results to make sure that the nal lists were non-
redundant. The sources of all direct associations are sum-
marized in Supplementary Table S3.
Indirect associations among SymMap components
In addition to six direct associations involving adjacent
components, there were nine indirect associations involv-
ing non-adjacent components (Figure 1A). We chose to in-
fer indirect associations from combinations of direct rela-
tionships. For example, the indirect relationships between
herbs and MM symptoms, can be obtained using the TCM
symptom as a middle component (Supplementary Figure
S2A). To remove possible false positives, we used Fisher’s
Exact Test (23) to obtain reliable associations with statis-
tical signicance, and Fisher’s exact test is effective and
widely used for evaluating the reliability of biomedical as-
sociations (24). Furthermore, to control the false discovery
rate (FDR) due to multiple tests, we calculated the FDRs
according to both the Bonferroni (25)andtheBH(26)
methods from P-values. The strategy was used for the in-
ference of four indirect associations that can be connected
through a middle component between them. For exam-
ple, the indirect associations for TCM symptom-ingredient,
herb-target, ingredient-disease, and MM symptom-target
relationships were inferred through herbs, ingredients, tar-
gets, and diseases, respectively (Supplementary Figure S2B-
E). Note that for herb-MM symptom and TCM symptom–
disease relationships, we did not perform tests, but retained
all associations using the intermediates TCM symptom and
MM symptom, respectively (Supplementary Figure S2A,
F). Because the intermediate relationships manually cu-
rated by experts were sufciently convincing to retain.
For the three remaining indirect associations, which
could have been connected by at least two components, it
was required that one component was selected as the inter-
mediate. For example, we selected the disease component
for the TCM symptom–target indirect associations and ap-
plied the same test procedure used above with only one mid-
dle component (Supplementary Figure S3A). The only dif-
ference between this procedure and that mentioned above
was that the statistical inferences of TCM symptom–target
relationships were based on the TCM symptom–disease re-
lationship inferred previously, so the strategy had two steps.
Similarly, the indirect MM symptom–ingredient associa-
tions were inferred in a step-wise manner using TCM symp-
toms as the intermediate (Supplementary Figure S3B). It
is noteworthy that the herb-disease association was empha-
sized and obtained using three strategies because this rela-
tionship is an important guide for PDD (Supplementary
Figure S3C). Firstly, we manually curated a small num-
ber of herb–disease relationships from the CHPH, as the
indications for herbs in CHPH contain a fraction of dis-
ease information (Supplementary Figure S1). Secondly, we
used ingredients as the intermediate to conduct two-step
testing. Thirdly, we used MM symptoms as the intermedi-
ate to conduct two-step testing. For herb–disease pairs that
were inferred by multiple methods, the smallest P-values
and FDRs were selected as condence scores. We should
note that the one component chosen as the intermediate is
selected based on empirical knowledge. The sources of all
indirect associations are summarized in Supplementary Ta-
ble S4.
Implementation of SymMap
In summary, SymMap provides information about six com-
ponents related to TCM and MM and their pairwise re-
lationships using a convenient web interface from which
users can browse, search, visualize and download data.
SymMap is free to access at http://www.symmap.org and
https://www.bioinfo.org/symmap without user registration.
The SymMap website was built using the Python-Flask
and Nginx frameworks. The SymMap data are stored in a
MySQL database. The SymMap website is compatible with
most major browsers.
DATABASE CONTENTS AND ACCESS
Database statistics
The six curated components in SymMap include 1717 TCM
symptoms, 961 MM symptoms, 499 herbs, 19 595 ingredi-
ents, 4302 targets and 5235 diseases (Table 1). The six types
of direct associations in SymMap include 6638 herb–TCM
symptom associations, 2978 TCM symptom–MM symp-
tom associations, 48 372 herb–ingredient associations, 12
107 MM symptom–disease associations, 29 370 ingredient–
target associations and 7256 gene–disease associations (Fig-
ure 1A). The distributions of the connections for each type
of direct association are shown in Figure 2A. For example,
in the TCM symptom–herb associations, each herb is as-
sociated with 13.30 TCM symptoms on average, and each
TCM symptom is associated with 3.87 herbs on average. For
the TCM symptom-MM symptom mapping introduced by
SymMap, each TCM symptom is associated with 1.74 MM
symptoms, and each MM symptom is associated with 3.13
TCM symptoms. The details of all direct associations are
shown in Supplementary Table S3.
The compositions of all nine indirect associations are
summarized in Supplementary Table S4. As expected, the
Bonferroni method for multiple testing correction was quite
strict and gave rather small set of predictions. So we mainly
chose the result from the BH method for representation.
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Nucleic Acids Research, 2018 5
Figure 2. Characteristics of the SymMap integrative network. (A) Box plots show the distribution of association numbers per item for six direct associations.
For each association between component 1–component 2, two boxes are shown. The rst box in blue shows the distribution of component 1, whereas the
second box in orange shows the distribution of component 2. (B) Bar plots show the total number of associations for seven indirect associations. For each
association, three bars are shown. The rst bar in blue shows the full set, the second bar in orange show the loosely selected set (P-value <0.05), and
the third bar in green shows the stringently selected set (FDR BH <0.05). (C) The sources of indirect herb-disease associations are shown. Associations
inferred via symptoms are shown in blue, whereas those inferred via ingredients are shown in orange, those inferred via both symptoms and ingredients
are shown in green, and those also be curated manually are shown in red. (D) The distribution of node degrees in the heterogeneous network of SymMap,
with direct associations shown in blue and indirect associations shown in orange.
For herb-MM symptom and TCM symptom–disease rela-
tionships, we provided all possible associations by network
neighbor extension (full set) because no statistical tests were
conducted. For the other seven types of indirect associ-
ation, we compared three datasets, including all possible
associations (full set), statistically signicant associations
with loose criteria (selected set, P<0.05), and statistically
signicant associations with stringent criteria (selected set,
FDR BH <0.05) (Figure 2B). The total number of asso-
ciations was reduced as stricter criteria were adopted. For
example, the full set of TCM symptom–ingredient associa-
tions contains 576 129 associations, but applying a P-value
cut-off of 0.05 leaves 275 097 associations, whereas applying
a FDR (BH) cutoff of 0.05 leaves 99 546 associations.
The herb–disease relationships were merged from several
paths and consist of 11 854 reliable associations in the strin-
gently selected set (FDR BH <0.05). We found that 4.35%
of the herb-disease associations were inferred by using both
MM symptoms and ingredients as the intermediates (Fig-
ure 2C). The same pattern was also observed for the full
set (Supplementary Figure S4A), the loosely selected set
(Supplementary Figure S4B). The two paths for connecting
herb–disease relationships via symptoms or ingredients can
more or less be analogized to PDD and TDD. The SymMap
data reveal that phenotype information facilitated the dis-
covery of ethnopharmacological candidates, which will pro-
vide a valuable resource for translational medicine stud-
ies. Furthermore, we found that 35.34% (P-value <0.05)
and 31.95% (FDR BH <0.05) of herb–disease associa-
tions from manual curation can also be inferred from sta-
tistical inference (Supplementary Table S5), which further
demonstrated the reliability of the statistical methods used
in SymMap.
Finally, we integrated all six components of SymMap,
as well as their pairwise relationships, including both di-
rect and indirect associations, with the latter chosen from
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6Nucleic Acids Research, 2018
the stringently selected set with a FDR (BH) smaller than
0.05. We thus built a heterogeneous network including 32
281 nodes and 403 318 edges, with 106 721 edges represent-
ing direct associations and 296 597 edges representing in-
direct associations. The distributions of node degrees for
the direct and indirect associations are quite similar (Fig-
ure 2D). Most nodes have a degree lower than 20, with a
ratio of 95.98% for direct associations and 79.02% for in-
direct associations, which shows that the network is sparse
in most parts. Furthermore, we analyzed the shared molec-
ular interactions on disease–symptom associations using a
previously published method (6). Consistent with previous
observations, we found that when diseases are more similar
in term of shared symptoms, they tended to be linked with
each other through the underlying genes in their PPI net-
work (Supplementary Figure S5). It further demonstrates
the value of SymMap in connecting external symptom map-
ping and internal molecular mechanisms.
Functionality of SymMap
Users can browse, search and download the six components
and their pairwise relationships through the SymMap web
interface (Figure 3A). Users can click the search button in
the homepage, input a query term in the search page to
execute the search. A different search box is provided for
each of the components of SymMap, with multiple types of
search keys provided. For example, to search for a specic
MM symptom, three types of search keys are permitted,
including the symptom name, the external ID in a widely
accepted database, and multiple aliases that are collected
from diverse databases for the convenience of the users.
All types of allowable search keys are described under the
search boxes and further explained in the download page.
And users can download all search terms in key les pro-
vided by SymMap. Furthermore, users can select similar
keys immediately after inputting query terms using the au-
tocomplete search functionality included in SymMap.
After searching SymMap, matches for the input query
terms are displayed in the lower part of the search page in
a summary table with the SymMap ID as the rst column.
Users are encouraged to click the hyperlink on the SymMap
ID for detailed information. In the details page, we provide
descriptive information and relationships with other com-
ponents using network visualizations and tables. Further-
more, a list of all items in each of the six components can be
navigated in the browse page, and these lists are also down-
loadable in the website.
Using the SymMap database
After browsing or searching SymMap, users can click the
SymMap ID for each specic term to jump onto the details
page, which provides a summary panel including descriptive
information, a network panel visualizing the all-versus-all
relationships among the six components, and a list panel
showing tables of related items for the selected search key.
The summary panel displays descriptive information for the
search item (Figure 3B). In general, we provide three types
of information: identication information (e.g. name and
gene symbol), explanatory information (e.g. denition and
class), and external IDs in other databases, which can be
clicked directly to navigate into the database.
Next, the network panel provides a visualization of all
related components for the search term (Figure 3C). The
nodes in the network are colored and placed in different
locations according to the source of the component. The
node size is customized according to its degree in the net-
work. When the user holds the mouse pointer over a node,
the node will be enlarged, its related edges will be high-
lighted, and its ID and name will be shown in a balloon.
In addition, each node in the picture can be hyperlinked to
its own details page. We further provided control panels for
users to change the network layout, to zoom in and out, and
to download the network picture. To avoid the presence of
an excessive number of nodes in a network, we chose the
stringent selected set of indirect associations with FDR BH
<0.05 for the network visualization.
Finally, the list panel in the bottom of the detail page
(Figure 3D) shows the information for the network visual-
ization in tabular format, including ve tables shown to rep-
resent ve related components other than its own compo-
nent. We provided three drop-down menus for users to cus-
tomize the visualization. Firstly, the ‘display’ menu allows
the users to select which relationship to access. Secondly,
the ‘select’ menu enables the users to choose which dataset,
including the full set and the subsets with different level of
statistical inference, should be listed in the page. Thirdly,
the ‘sort’ menu gives the users a capability to sort the items
in the table according to SymMap IDs, P-values, FDRs
(BH) and FDRs (Bonferroni). Furthermore, we added a
‘download’ button for users for bulk downloading.
DISCUSSION
A major goal of biomedical research is to elucidate
phenotype–genotype relationships. Symptom phenotypes
are diligently observed by physicians and are crucial for
accurate clinical diagnosis and treatment. TCM symptoms
have been utilized in clinical applications for millennia in
a relatively large number of individuals. Therefore, TCM
symptom–herb relationships provide tremendously valu-
able guidance for drug discovery programs. In this report,
we present SymMap, a comprehensive database integrat-
ing TCM with MM via external symptom mapping and
internal molecular mechanisms. The TCM symptoms in
in SymMap, as well as their relationships with herbs and
MM symptoms, were manually curated by a committee of
17 leading TCM experts. SymMap is the rst publically
available database containing comprehensive information
regarding the relationships between TCM symptoms, TCM
herbs and MM symptoms. Furthermore, users can access
all-versus-all pairwise relationships between any two com-
ponents in SymMap as direct associations obtained from
database integration or indirect associations inferred based
on statistical tests. Therefore, we have combined phenotype-
based and target-based knowledge in SymMap to pro-
mote efcient phenotype-based compound screening un-
der the guidance of current knowledge about targets for
compounds and diseases. Users can easily access, navigate,
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Nucleic Acids Research, 2018 7
Figure 3. An illustration of the SymMap search. (A) The index page of SymMap shows the database overview. (B) The summary panel in the details page
shows descriptive information for the search item. (C) The network panel in the details page shows all related components for the search item, with nodes
colored by their source component. Holding the mouse pointer over the node highlights the node and its related edges, while showing its ID and name, as
well as a link to its details page. (D) The list panel shown in tables. For each search item, ve tables can be selected for the ve other related components.
For each table, three datasets can be selected by the users: the full set, the loosely selected set with P-values smaller than 0.05, and two stringently selected
sets with FDRs (Bonferroni and BH) smaller than 0.05. All related components can be downloaded by pressing the button at the upper right.
and visualize these data, as well as the relationships be-
tween database components, using the website interface for
the SymMap database. We plan to continue to add data to
SymMap as additional information becomes available, as
well as to improve the user experience at the SymMap web-
site.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENTS
We thank Kui Xu for help with the network visualization.
We thank Dr Zhen Li and Dr Zhaoqing Ba for critical com-
ments during manuscript preparation.
FUNDING
National Key Research and Development Program
of China [2018YFC1313000, 2018YFC1313001,
2017YFC1703506]; National Natural Science Foun-
dation for Young Scholars of China [31701141, 31701149,
31501066]; National Natural Science Foundation of China
[91740113, 81522051]. Innovation Project for Institute of
Computing Technology, CAS [20186060]. Funding for
open access charge: National Natural Science Foundation
of China.
Conict of interest statement. None declared.
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... [15], SymMap (http://www.symmap.org) [16], Integrative Pharmacology-Based Research Platform of TCM (TCMIP, http://www.tcmip.cn/TCMIP/index.php/Home/Login/login .html) ...
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Online Mendelian Inheritance in Man (OMIM™) is a comprehensive, authoritative and timely knowledgebase of human genes and genetic disorders compiled to support human genetics research and education and the practice of clinical genetics. Started by Dr Victor A. McKusick as the definitive reference Mendelian Inheritance in Man, OMIM (http://www.ncbi.nlm.nih.gov/omim/) is now distributed electronically by the National Center for Biotechnology Information, where it is integrated with the Entrez suite of databases. Derived from the biomedical literature, OMIM is written and edited at Johns Hopkins University with input from scientists and physicians around the world. Each OMIM entry has a full-text summary of a genetically determined phenotype and/or gene and has numerous links to other genetic databases such as DNA and protein sequence, PubMed references, general and locus-specific mutation databases, HUGO nomenclature, MapViewer, GeneTests, patient support groups and many others. OMIM is an easy and straightforward portal to the burgeoning information in human genetics.
Article
The Medical Subject Headings (MeSH) is the National Library of Medicine (NLM) controlled vocabulary for indexing articles. Inaccuracies in the MeSH thesaurus have been reported for several areas including pharmacy. To assess the quality of pharmacy-specific MeSH assignment to articles indexed in pharmacy journals. The 10 journals containing the highest number of articles published in 2012 indexed under the MeSH 'Pharmacists' were identified. All articles published over a 5-year period (2008-2012) in the 10 previously selected journals were retrieved from PubMed. MeSH terms used to index these articles were extracted and pharmacy-specific MeSH terms were identified. The frequency of use of pharmacy-specific MeSH terms was calculated across journals. A total of 6989 articles were retrieved from the 10 pharmacy journals, of which 328 (4.7%) were articles not fully indexed and therefore did not contain any MeSH terms assigned. Among the 6661 articles fully indexed, the mean number of MeSH terms was 10.1 (SD = 4.0), being 1.0 (SD = 1.3) considered as Major MeSH. Both values significantly varied across journals. The mean number of pharmacy-specific MeSH terms per article was 0.9 (SD = 1.2). A total of 3490 (52.4%) of the 6661 articles were indexed in pharmacy journals without a single pharmacy-specific MeSH. Of the total 67193 MeSH terms assigned to articles, on average 10.5% (SD = 13.9) were pharmacy-specific MeSH. A statistically significant different pattern of pharmacy-specific MeSH assignment was identified across journals (Kruskal-Wallis P < 0.001). The quality of assignment of the existing pharmacy-specific MeSH terms to articles indexed in pharmacy journals can be improved to further enhance evidence gathering in pharmacy. Over half of the articles published in the top-10 journals publishing pharmacy literature were indexed without a single pharmacy-specific MeSH. Copyright © 2014 Elsevier Inc. All rights reserved.