.

1. Dac Trung Nguyen, Timothy Sheils, Geetha Mandava, Noel Southall, Rajarshi Guha
NCATS, NIH
Putting Targets in Context

2. IDG Knowledge Management Center

3. Entity browsing (filterable & linked)Search (full text, auto-suggest)
Detailed view of entities Built on top of a robust REST API
An Interface to the KMC

4. Current Status
191 facets
17.8 GB database
30 GB Lucene indexes
36K LoC (Java)
14K LoC (Scala)
Image available
Source code available
20,120 targets
15,094 diseases
2.3M publications
4,500 drugs
Nguyen & Mathias et al, NAR, 2017
https://spotlite.nih.gov/ncats/pharos/issues
https://hub.docker.com/r/ncats/pharos/

5. What’s Included?
• Pharos presents data from a variety of sources,
integrated by U. New Mexico
• Primary focus is the protein target
• Wherever possible, targets are linked to other
entities (which are also interlinked)
Ø Small molecules, Diseases, Publications
• Target related data include
Ø Identifiers, ontology terms, sequence, expression data,
publications (curated & text mined), phenotypes, PPI

6. Data Sources
Full data source list at
http://targetcentral.ws/Pharos
Full data source list at http://targetcentral.ws/Pharos

7. Biologists &
Clinical Researcher
• Characterize &
validate novel
targets
• Identify key small
molecules or
biologics
Informatics
Scientists
• Data mining
• Support target
validation
projects
Program Staff
• Explore the
research
landscape
• New directions
for research &
funding
Target Audience

8. Do You Know What You Want?
• Efficient full text search
• Primary entry point when exploring and for
hypothesis generation
• Fast autosuggestion facility
Ø Suggestions grouped
by type (disease,
ligand, …)
• Searches run across
all entity types
Ø But can be restricted
to specific ones

9. Multiple Search Options
Batch search Sequence search
Structure searchText search

10. (Possibly) Lots of Results

11. Filters

12. Visualization
• Key requirement for efficient exploration, summary
• Increase information density in limited screen real
estate, take context into account
• Interactivity is desirable, high quality for easy
inclusion in documents
• Simple is better than fancy but pretty pictures
have value, make for a better experience
• Integrate and link to external visualization

13. Visualization Highlights
Visualization dashboard – filters appropriately
represented, plots act as filters
Inline visualization to increase information density
Summary visualizations
overlay multiple dimensions
and can be context aware

14. Integrating External Tools
Tclin, Kinase
Tdark, GPCR
Pharos
TinX

15. Documentation

16. Entity Dossier
Multiple dossiers
Set operationsVisualization tools
Download

17. Dossiers as Context
Overlay data from targets in a dossier

18. Use Case – Targets for Obesity
20K targets 3K targets
616 targets
4 targets
ALPK1 7 targets listed in
5R01NS044385-14
KIF7
Disease
Obesity
GWAS Trait
Obesity
IDG Family
GPCR/IC/
Kinase
Grants
5R01NS...

19. Use Case - Target Similarity
• Find understudied
targets that have
similar data
profiles to well
studied targets
• Supports
recommendations,
prioritization
Tdark targets whose most
similar target is not Tdark

20. Outreach & Dissemination Activities
User Feedback Deployment
Webinars Documentation
NER API for
targets & diseases
@idg_pharos
Recent papers to
Pharos links via
Tweets

21. Pharos Usage
• Usage statistics over
the last one year
are generally
increasing
• 89K pageviews
• 14K sessions
• 7.5K users

22. The Long Term Vision
• Incorporate dependencies
between data types to support
inference and sophisticated filters
• From presentation to summarization
Ø Use explicit links & computational
inference to generate (semi-) natural language
summary using all known data
Ø Influenced by the query
• The result is a biological dashboard,
customized for the user and the query
Target X has been implicated in 3
diseases related to skeletal, urological
and nervous systems. It has been
investigated in 5 in vitro assay, 2 in
vivo assays. There are 4 compounds
active against this target, 3 of which
are in clinical trials.

23. Feedback
• Explore the UI, try it, break it, and let us know
what works and what doesn’t
• Are there data types and relations that would help
you but are not available?
https://pharos.nih.gov
pharos@nih.gov
@idg_pharos

24. Acknowledgements
Dac-Trung Nguyen, Kyle Brinacombe, Timothy
Sheils, Geetha Mandava, Noel Southall, Ajit Jadhav
Steve Mathias, Oleg Ursu, Jeremy Yang,
Christian Bologa, Daniel Canon, Tudor Oprea
Nicholas Fernandez, Andrew Rouillard, Avi Mayan
Ajay Pillai, Aaron Pawlyk, Christine Colvis
Tomita Lab / Finkbeiner lab