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2 2 2 Agenda Shark Tank Overview SEMOSS HHS Ignite Use Case Demo

3 3 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom HHS Ignite is an “incubator for new ideas” run out of the HHS IDEA Lab. NIAID SEB Innovation Challenge HHS Ignite Innovation Program Expansion Jan ‘14 Jul ‘14Aug ‘14Sep ‘14 Nov ‘14 The Evolution of HHS Ignite Phase 1: HHS Ignite boot camp Phase 2: NIH Interviews & Pilot Development Phase 3: HHS Ignite Shark Tank Vincent Munster, PhD Peter Jahrling, PhD NIH SEMOSS team with HHS Deputy Secretary Bill Corr & HHS CTO Bryan Sivak Responding to an informal request for innovation ideas from the NIH’s National Institute of Allergy and Infectious Diseases (NIAID), a small Deloitte team submitted a written proposal. At the client’s suggestion, the proposal was submitted and the team selected to compete the 2014 HHS IdeaLab’s Ignite innovation tournament. During the 3-month pilot, the Deloitte team engaged 10 NIAID customers and created a functional proof-of-concept solution for 2 intramural scientists. The Deloitte/NIAID team successfully presented their pitch to a panel of relevant federal executives and the HHS CTO during the concluding Shark Tank on September 30, 2014.

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5 5 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom SEMOSS Evolution Solution History SEMOSS is a result of several years of federal investment in federated, semantic web technology. In 2010, the Deputy Chief Management Officer (DCMO) of the Department of Defense began experimenting with Semantic Web technology. Military Health System (MHS) with help from Deloitte created a graph-based toolset for multi-dimensional analysis of disparate data sources to determine investment sequencing for the MHS IT portfolio. After MHS presented its solution to DCMO, a joint investment was made to fund a similar tool utilizing the Semantic Web. The guiding principles of the tool were that it must be standards based; allow integrating data from multiple sources; and adopt visualizations and analytics on an as-needed basis. This investment spawned SEMOSS. Analysis Data Sources Excessive time spent on data preparation analysis and visualization constraints 1-2 Data Sources3-4 Data Sources Limited to single database Answers modeled as graph traversals demonstrations Stakeholders 2-3 No Limit Integrated knowledge analytics environment Transitive across databases Collaboration Answers modeled as reports No Limit Knowledge Exploration Minimal Repetitive Visualizations Single Dimensional Difficult to customize Multi-Dimensional Self Service Excel / Tableau 2011 – 2012 Neo4J2012 – 2013 SEMOSS 1 Issues Focus on visualization Not Malleable Proprietary Long cycle times None as product created to meet client needs Solution Evolution

6 6 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom What Does Federated Analytics Mean? Elastic data integration with more than 6 connectors, including Excel/CSV, NLP, RDBMS, Cloud Aware Data sources Context aware data, that can link across databases W3C Standards – RDF, SPARQL Rich library of visualizations Parallel Coordinates Excel style charting Network Viz. Heat-maps Extensibility to adopt any visualization Overlay visualizations to see overlaps Graph Algorithms Optimization – Linear and Non- Linear algorithms Statistical algorithms Equation Solving Data Viz. Analytics alytics Federate Data Federate Data Discover Insights Perform Analysis Visualize Decisions Share Knowledge

7 7 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Types of Visualizations Included in SEMOSS

8 8 8 HHS IGNITE INNOVATION PROGRAM USE CASE

9 9 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Diverse Researchers across HHS ‏ Dawei Lin PhD, NIH Computer Modeling ‏ Vincent Munster PhD, NIH Infectious Diseases ‏ Marie Parker, NIH Research Initiatives ‏ Susanna Visser DrPh, CDC ADHD

10 10 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Common Research Goals ‏ Dawei Lin PhD, NIH Computer Modeling ‏ Vincent Munster PhD, NIH Infectious Diseases ‏ Marie Parker, NIH Research Initiatives ‏ Susanna Visser DrPh, CDC ADHD Data Access Robust Analysis Collaboration

11 11 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Technology Barriers ‏ Dawei Lin PhD, NIH Computer Modeling ‏ Vincent Munster PhD, NIH Infectious Diseases ‏ Marie Parker, NIH Research Initiatives ‏ Susanna Visser DrPh, CDC ADHD Big Data Inaccessibility Isolated Analysis Collaboration Barriers Multiple Sources Integration Challenges

12 12 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Dr. Munster’s Research ‏ Vincent Munster PhD, NIH Infectious Diseases Big Data Inaccessibility Isolated Analysis Collaboration Barriers Multiple Sources Integration Challenges Middle East Respiratory Syndrome (MERS) The platform allows me to analyze and grasp large seemingly incomprehensible datasets. - Vincent Munster, PhD

13 13 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Dr. Munster’s Research Challenges 1)Diseases 2)Articles 3)Collaborators Private Data PubMed FAERS DisGeNet PharmGKB HGNC

14 14 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Our Tested Solution 1)Diseases 2)Articles 3)Collaborators Private Data PubMed FAERS DisGeNet PharmGKB HGNC

15 15 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Use Case Metamodel CTD PharmGKB PubMed DisGeNet DrugBank HGNC PubChem Private Datasets Gene Publication Author Chemical Disease Drug Drug Component Researcher Datasets Pathway Molecular Function Biological Process Chromosome Cell Component No single database has exhaustive information. Multiple connections ensure complete data. The data sources above reflect the information requested by our customer. This solution can be easily customized for any researcher.

16 16 HHS IGNITE DEMO

17 Appendix

18 18 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom SEMOSS Supplementing Insights 1.Private Research Data 2.Online Mendelian Inheritance in Man (OMIM) 3.PubMed 4.HUGO Gene Nomenclature Committee (HGNC) 5.DrugBank 6.Comparative Toxicogenomics Database (CTD) 7.Disease Gene Network (DisGeNet) 8.PubChem 9.PharmGKB 1.Gene Expression 2.Chemical 3.Cellular Pathway 4.Molecular Function 5.Biological Process 6.Cytolocation 7.Cell Component 8.Gene Nomenclature 9.Disease 10.Publication 11.Author Relevant Data Data Sources

19 19 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Solution Benefits & Capabilities Researcher Benefits Data Accuracy; ensure you are using validated, authoritative sources Time Efficiency; eliminate days spent reading publications and searching for data Single Platform; use centralized platform rather than multiple data locations Rapid Visualization & Analysis; to gain insight and accelerate research Scientific Collaboration; secure public/private cloud instance for collaboration  Solution Capabilities Big Data; navigate and distill relevant data seamlessly Extensible, Scalable Data Model; shared model of understanding Undirected Research; what questions do we ask public data that we do not have answers to? Broad Applicability; across many subject areas and data types Open Data Initiatives; federal public data initiatives with no data consumption tool

20 20 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom SEMOSS maximizes HHS Open Data ROI by leveraging the vast networks of public and private life science data to promote insight and discovery. SEMOSS Federal Health Data Environment Cloud Infrastructure SEMOSS Platform End Users Scientific Use CaseSEMOSS Solution SEMOSS Which diseases are associated with my genes of interest? PharmGKB CTD PubMed DisGeNet Cancer Researcher HGNC MESH FAERS Solution Overview

21 21 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Solution Demonstration 1)Diseases 2)Articles 3)Collaborators Data Sources 1.Private Data 2.HGNC 3.OMIM 4.DisGeNet 5.CTD 6.PharmGKB 7.PubMed 8. FAERS 9. VAERS

22 22 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Solution Demonstration Data Sources 1.Private Data 2.HGNC 3.OMIM 4.DisGeNet 5.CTD 6.PharmGKB 7.PubMed 8. FAERS 9. VAERS 1)Diseases 2)Articles 3)Collaborators

23 23 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Solution Demonstration 1)Diseases 2)Articles 3)Collaborators Data Sources 1.Private Data 2.HGNC 3.OMIM 4.DisGeNet 5.CTD 6.PharmGKB 7.PubMed 8. FAERS 9. VAERS

24 24 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom Solution Demonstration 1)Diseases 2)Articles 3)Collaborators Data Sources 1.Private Data 2.HGNC 3.OMIM 4.DisGeNet 5.CTD 6.PharmGKB 7.PubMed 8. FAERS 9. VAERS The platform allows me to analyze and grasp large seemingly incomprehensible datasets. - Vincent Munster, PhD

25 25 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom SEMOSS Supplementing Insights Identify Question SEMOSS pre-packages more than eighty questions across domains that can be readily utilized. New questions can be modeled as reports. Synthesize Meta Model SEMOSS has more than ten different domain metamodels. New models can be created / extended to emulate mental models. Find and Import Data SEMOSS has industry data across healthcare, infrastructure, data and BPR that can be readily explored. Link excel data or RDBMS to existing data for analysis Find and Import Data SEMOSS has industry data across healthcare, infrastructure, data and BPR that can be readily explored. Link excel data or RDBMS to existing data for analysis. Visual Analysis Visual Analysis SEMOSS allows automatic linking of data across databases and allows cross-database visualization. Users no longer need to import everything into a single database.

26 26 Horizontal Margin (9.13”) Strapline Content Bottom Content w/out Strapline Bottom The Team ‏ Special Thanks to… Prabhu Kapaleeswaran Author, SEMOSS MHS Joe Croghan Project Supervisor NIH Brock Smith Project Lead NIH Alexander Sherman Technical SME NIH Karthik Balakrishnan Technical Lead NIH LeeAnn Bailey, PhD Science SME FDA Alexandra Kwit Science Lead NIH Regina Cox Data SME CDC Vincent Munster, PhD NIH Mike Tartakovsky NIH Alex Rosenthal NIH Dawei Lin, PhD NIH Peter Jahrling, PhD NIH David Parrish NIH


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