=Paper=
{{Paper
|id=None
|storemode=property
|title=Reflections on: Finding Melanoma Drugs Through a Probabilistic Knowledge Graph
|pdfUrl=https://ceur-ws.org/Vol-2576/paper07.pdf
|volume=Vol-2576
|authors=James P. McCusker,Michel Dumontier,Rui Yan,Sylvia He,Jonathan S. Dordick,Deborah L. McGuinness
|dblpUrl=https://dblp.org/rec/conf/semweb/McCuskerDYHDM19
}}
==Reflections on: Finding Melanoma Drugs Through a Probabilistic Knowledge Graph==
https://ceur-ws.org/Vol-2576/paper07.pdf
Reflections on: Finding Melanoma Drugs
Through a Probabilistic Knowledge Graph
James P. McCusker1 , Michel Dumontier4 , Rui Yan1 , Sylvia He1 , Jonathan S.
Dordick2,3 , and Deborah L. McGuinness1 , 3
1
Department of Computer Science,
2
Department of Chemical & Biological Engineering,
3
Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic
Institute, Troy, NY, US
4
Stanford Center for Biomedical Informatics Research, Stanford University,
Stanford, CA, US
Abstract. We build a nanopublication-based knowledge graph of pro-
tein/protein, drug/protein, and protein/disease interactions to create a
resource for exploring potential therapies for diseases. This is accom-
plished using Semantic Web standard tools like Blazegraph, SADI web
services, and JSON-LD for integration with a Javascript-based web client.
Metastatic cutaneous melanoma is an aggressive skin cancer with some
progression-slowing treatments but no known cure. The omics data ex-
plosion has created many possible drug candidates, however filtering cri-
teria remain challenging, and systems biology approaches have become
fragmented with many disconnected databases. Using drug, protein, and
disease interactions, we built an evidence-weighted knowledge graph of
integrated interactions. Our knowledge graph-based system, ReDrugS,
can be used via an API or web interface, and has generated 25 high
quality melanoma drug candidates. We show that probabilistic analysis
of systems biology graphs increases drug candidate quality compared to
non-probabilistic methods. Four of the 25 candidates are novel therapies,
three of which have been tested with other cancers. All other candidates
have current or completed clinical trials, or have been studied in in vivo
or in vitro. This approach can be used to identify candidate therapies for
use in research or personalized medicine.
Keywords: melanoma, drug repositioning, knowledge graphs, uncertainty rea-
soning
1 Introduction
Metastatic cutaneous melanoma is an aggressive cancer of the skin with low
prevalence but very high mortality rate, with an estimated 5 year survival
rate of 6 percent [1] There are currently no known therapies that can consis-
tently cure metastatic melanoma. Vemurafenib is effective against BRAF mu-
tant melanomas [2] but resistant cells often result in recurrence of metastases
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 Authors Suppressed Due to Excessive Length
[8] Melanoma itself may be best approached based on the individual genetics of
the tumor, as it has been shown to involve mutations in many different genes to
produce the same disease [7]. Because of this, an individualized approach may
be necessary to find effective treatments.
A knowledge graph is a compilation of facts and figures that can be used to
provide contextual meaning to searches. Google is using knowledge graphs to
improve its search and to analyze the information graph of the web; Facebook is
using them to analyze the social graph. We built our knowledge graph with the
goal of unifying large parts of biomedical domain knowledge for both mining and
interactive exploration related to drugs, diseases, and proteins. Our knowledge
graph is enhanced by the provenance of each fragment of knowledge captured,
which is used to compute the confidence probabilities for each of those fragments.
Further, we use open standards from the World Wide Web Consortium (W3C),
including the Resource Description Framework (RDF) [6], Web Ontology Lan-
guage (OWL) [12], and SPARQL [4]. The representation of the knowledge in our
knowledge graph is aligned with best practice vocabularies and ontologies from
the W3C and the biomedical community, including the PROV Ontology [9], the
HUPO Proteomics Standards Initiative Molecular Interactions (PSI-MI) Ontol-
ogy [5], and the Semanticscience Integrated Ontology (SIO) [3]. Use of these
standards, vocabularies, and ontologies make it simple for ReDrugS to integrate
with other similar efforts in the future with minimal effort.
We built a novel computational drug repositioning platform, that we refer
to as ReDrugS, that applies probabilistic filtering over individually-supported
assertions drawn from multiple databases pertaining to systems biology, phar-
macology, disease association, and gene expression data. We use our platform to
identify novel and known drugs for melanoma.
2 Results
We used ReDrugS to examine the drug-target-disease network and identify
known, novel, and well supported melanoma drugs. The ReDrugS knowledge
base contained 6,180 drugs, 3,820 diseases, 69,279 proteins, and 899,198 interac-
tions.
We examined drug and gene connections that were 3 or less interaction steps
from melanoma, and additionally filtered interactions with a joint probability
greater or equal to 0.93. We identified 25 drugs in the resulting drug-gene-disease
network surrounding melanoma as illustrated in Figure 1 .
We then validated the set of 25 drugs by determining their position in the
drug discovery pipeline for melanoma. Nearly all drugs uncovered by ReDrugS
were previously been identified as potential melanoma therapies either in clinical
trials or in vivo or in vitro. Of the 25 drugs, 12 have been in Phase I, II, or III
clinical trials, 5 have been studied in vitro, 4 in vivo, 1 was investigated as a
case study, and 3 are novel.
To further evaluate our system, we examined the impact of decreasing the
joint probability or increasing the number of interaction steps. Figures 2 A and
Title Suppressed Due to Excessive Length 3
Fig. 1. The interaction graph of predicted melanoma drugs with a probability of 0.93 or
higher and have three or fewer intervening interactions between drug and disease. The
“Explore” tab contains the controls to expand the network in various ways, including
the filtering parameters. Node and edge detail tabs provide additional information
about the selected node or edge, including the probabilities of the edges selected. Users
can control the layout algorithm and related options using the “Options” tab.
B show precision, recall, and f-measure curves while varying each parameter.
Using these information retrieval performance curves we found that using a
joint probability of 0.93 or greater with 3 or less interaction steps maximizes the
precision and recall as shown in Figure 2.
By performing a literature search on hypothesis candidates with a joint prob-
ability of 0.5 or higher and 6 or fewer interaction steps, we were able to generate
precision, recall, and f-measure curves for both cutoffs to find our cutoff of 0.93
with 3 or fewer interaction steps. The precision, recall, and f-measure curves
are shown for varying joint probability thresholds in Figure 2 A and for varying
interaction step counts in Figure 2 B.
3 Discussion
We designed ReDrugS to quickly and automatically integrate and filter a het-
erogeneous biomedical knowledge graph to generate high-confidence drug reposi-
tioning candidates. Our results indicate that ReDrugs generates clinically plau-
4 Authors Suppressed Due to Excessive Length
(A) Information Retrieval by Probability Threshold (B) Information Retrieval by Network Expansion Step
precision recall fmeasure precision recall fmeasure
0.75 0.75
0.5 0.5
0.25 0.25
0.9 0.8 0.7 0.6 3 4 5
Fig. 2. Precision, recall, and f-measure by (A) varying thresholds for joint probability
and (B) varying number of interaction steps. Precision is the percentage of returned
candidates that have been validated experimentally or have been in a clinical trial (a
“hit”) versus all candidates returned. Recall is the percentage of all known validated
“hits”. F-measure is the geometric mean of precision and recall that provides a balanced
evaluation of the quality and completeness of the results.
sible drug candidates, in which half are in various stages of clinical trials, while
others are novel or are being investigated in pre-clinical studies. By helping to
consolidate the three main datatypes - drug targets, protein interactions, and
disease genes, ReDrugs can amplify the ability of researchers to filter the vast
amount of information into those that are relevant for drug discovery.
3.1 Architecture
ReDrugS uses a fairly straightforward web architecture, as shown in Figure 3.
It uses the Blazegraph RDF database backend. The database layer is inter-
changeable except that the full text search service needs to use Blazegraph-only
properties to perform text searches as text indexing is not yet standardized in
the SPARQL query language. All other aspects are standardized and should
work with other RDF databases without modification. ReDrugs currently uses
the Python-based TurboGears web application framework hosted using the Web
Services Gateway Interface (WSGI) standard via an Apache HTTP server. Tur-
boGears in turn hosts the SADI web services that drive the application and
access the database. It also serves up the static HTML and supporting files.
The user interface is implemented with AngularJS and Cytoscape.js, which
submits queries to the SADI web services using JSON-LD and aggregates results
into the networked view. The software relies exclusively on standardized proto-
cols (HTTP, SADI, SPARQL, RDF, and others) to make it simple to replace
technologies as needed. The data itself is processed using conversion scripts as
shown in Figure 4.
Title Suppressed Due to Excessive Length 5
Javascript Web Client
Cytoscape.js
JSON-LD
/api/search
/api/upstream
/api/downstream
Python + Apache Web Server SPARQL
RDF Store
Fig. 3. The ReDrugS software architecture. Using web standards and a three layer
architecture (RDF store, web server, and rich web client), we were able to build a
complete knowledge graph analysis platform.
4 Materials and Methods
This research project did not involve human subjects. The ReDrugS platform
consists of a graphical web application, an application programming interface
(API), and a knowledge base. The graphical web application enables users to
initiate a search using drug, gene, and disease names and synonyms. Users can
then interact with the application to expand the network at an arbitrary number
of interactions away from the entity of interest, and to filter the network based on
a joint probability between the source and target entities. Drug-protein, protein-
protein, and gene-disease interactions were obtained from several datasets and
integrated into ontology-annotated and provenance and evidence bearing rep-
resentations called nanopublications. The web application obtains information
from the knowledge base using semantic web services. Finally, we evaluated
our approach by examining the mechanistic plausibility of the drug in having
melanoma-specific disease modifying ability. We evaluated a large number of pos-
sible drug/disease associations with varying joint probabilities and interaction
steps to determine the thresholds with the highest F-Measure, resulting in our
thresholds of three or less interactions and a joint probability of 0.93 or higher.
4.1 Semantic Web Services
We developed four Semantic Automated Discovery and Integration (SADI) web
services [13] in Python to support easy access to the nanopubications (see Table
1) in ReDrugS. The four services are enumerated in Table 1.
6 Authors Suppressed Due to Excessive Length
Ontological Resources
Protein/Protein Interaction Ontology,
Semanticscience Integrated Ontology, Experimental
Gene Ontology
Method
vocabularies, relationships Assessment
evidence to Confidence scores of
probability
iRefIndex converted to
nanopubs
experimental methods.
ReDrugS
RDF Store
queries
ReDrugS API
Interaction network search
and expansion
queries graph queries graph
Analytical Tools ReDrugS
Cytoscape, R, Python, etc. Cytoscape.js App
Fig. 4. The ReDrugS data flow. Data is selected from external databases and converted
using scripts into nanopublication graphs, which are loaded into the ReDrugS data
store. This is combined with experimental method assessments, expressed in OWL,
and public ontologies into the RDF store. The web service layer queries the store
and produces aggregate analyses of those nanopublications, which is consumed and
displayed by the rich web client. The same APIs can be used by other tools for further
analysis.
The first service is a simple free text lookup, that takes an pml:Query 5 [10]
with a prov:value as a query and produces a set of entities whose labels contain
the substring. This is used for interactive typeahead completion of search terms
so users can look up URIs and entities without needing to know the details.
The other three SADI services look up interactions that contain a named
entity. Two of them look at the entity to find upstream and downstream con-
nections, and the third service assumes that the entity is a biological process
and finds all interactions that related to that process. The services return only
one interaction for each triple (source, interaction type, target). There are of-
ten multiple probabilities per interaction, and more than one interaction per
interaction type. This is because the interaction may have been recorded in mul-
tiple databases, based on different experimental methods. To provide a single
probability score for each interaction of a source and target, the interactions are
combined. A single probability is generated per identified interaction by tak-
ing the geometric mean of the probabilities for that interaction. However, this
5
PML 3, in development: https://github.com/timrdf/pml. This includes PML 2 con-
structs that are not covered in PROV-O.
Title Suppressed Due to Excessive Length 7
Service Description URL Input Output
Name
Resource Look up resources using search pml:Query pml:AnsweredQuery
text tearch free text search against
their RDFS labels. This
service is optimized for ty-
peahead user interfaces.
Find inter- Find interactions whose process sio:Process sio:Process
actions in participants or targets also
a biological participate in the input
process process.
Find up- Find interactions that the upstream sio:MaterialEntity sio:Target
stream input entity is a target of
participants in and have explicit partic-
ipants.
Find down- Find interactions that the downstream sio:MaterialEntity sio:Agent
stream tar- input entity participates in
gets and have explicit targets.
Table 1. The API endpoint prefix is http://redrugs.tw.rpi.edu/api/.
method is undesirable when combining multiple interaction records of the same
type. We instead combine the interaction records using a form of probabilistic
voting using composite Z-Scores. This is done to model that multiple experi-
ments that produce the same results reinforce each other, and should therefore
give a higher overall probability than would be indicated by taking their mean
or even by Bayes Theorem. We do this by converting each probability into a Z
Score (aka Standard Score) using the Quantile Function (Q()), summing the val-
ues, and applying the Cumulative Distribution Function (CDF ()) to compute
the corresponding probability:
n
!
X
P (x1...n ) = CDF Q (P (xi ))
i=1
These composite Z Scores, which we transform back into probabilities, are
frequently used to combine multiple indicators of the same underlying phenom-
ena, as in [11].
4.2 User Interface
The user interface was developed using the above SADI web services and uses
Cytoscape.js,6 , angular.js,7 and Bootstrap 3.8 An example network is shown
6
http://cytoscape.github.io/cytoscape.js
7
https://angularjs.org
8
http://getbootstrap.com
8 Authors Suppressed Due to Excessive Length
in Figure 1 Users can search for biological entities and processes, which can
then be autocompleted to specific entities that are in the ReDrugS graph. Users
can then add those entities and processes to the displayed graph and retrieve
upstream and downstream connections and link out to more details for every
entity. Cytoscape.js is used as the main rendering and network visualization tool,
and provides node and edge rendering, layout, and network analysis capabilities,
and has been integrated into a customized rich web client.
In order to evaluate this knowledge graph, we developed a demonstration
web interface9 based on the Cytoscape.js10 JavaScript library. The interface lets
users enter biological entity names. As the user types, the text is resolved to
a list of entities. The user finishes by selecting from the list, and submitting
the search. The search returns interactions and nodes associated with the entity
selected, which are added to the Cytoscape.js graph. Users are also able to select
nodes and populate upstream or downstream connections. Figure 1 is an example
output of this process.
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