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Http://www.semoss.org http://youtube.com/user/semossanalytics http://twitter.com/semossanalytics.

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Presentation on theme: "Http://www.semoss.org http://youtube.com/user/semossanalytics http://twitter.com/semossanalytics."— Presentation transcript:

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2 Agenda Shark Tank Overview SEMOSS HHS Ignite Use Case Demo

3 The Evolution of HHS Ignite
HHS Ignite is an “incubator for new ideas” run out of the HHS IDEA Lab. The Evolution of HHS Ignite NIAID SEB Innovation Challenge Expansion Jul ‘14 Aug ‘14 Sep ‘14 Jan ‘14 Nov ‘14 HHS Ignite Innovation Program 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. Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 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

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5 Knowledge Exploration
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. Solution Evolution Excel / Tableau 2011 – 2012 Neo4J 2012 – 2013 SEMOSS Analysis Excessive time spent on data preparation analysis and visualization constraints Limited to single database Answers modeled as graph traversals demonstrations Integrated knowledge analytics environment Transitive across databases Collaboration Answers modeled as reports Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Data Sources 1-2 Data Sources 3-4 Data Sources No Limit Stakeholders 1 2-3 No Limit Knowledge Exploration Minimal Repetitive Visualizations Single Dimensional Difficult to customize Multi-Dimensional Self Service Issues Focus on visualization Not Malleable Proprietary Long cycle times None as product created to meet client needs

6 What Does Federated Analytics Mean?
Data Perform Analysis Discover Insights Visualize Decisions Share Knowledge 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 Data Viz. Analytics Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Graph Algorithms Optimization – Linear and Non-Linear algorithms Statistical algorithms Equation Solving

7 Types of Visualizations Included in SEMOSS
Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015

8 HHS Ignite Innovation Program Use Case

9 Diverse Researchers across HHS
Public Health Science Research Bioinformatics Strategic Planning Vincent Munster PhD, NIH Infectious Diseases Dawei Lin PhD, NIH Computer Modeling Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Susanna Visser DrPh, CDC ADHD Marie Parker, NIH Research Initiatives

10 Data Access Robust Analysis Collaboration
Common Research Goals Public Health Science Research Bioinformatics Strategic Planning Vincent Munster PhD, NIH Infectious Diseases Dawei Lin PhD, NIH Computer Modeling Data Access Robust Analysis Collaboration Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Susanna Visser DrPh, CDC ADHD Marie Parker, NIH Research Initiatives

11 Collaboration Barriers
Technology Barriers Public Health Science Research Bioinformatics Strategic Planning Big Data Vincent Munster PhD, NIH Infectious Diseases Dawei Lin PhD, NIH Computer Modeling Multiple Sources Isolated Analysis Inaccessibility Integration Challenges Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Collaboration Barriers Susanna Visser DrPh, CDC ADHD Marie Parker, NIH Research Initiatives

12 Middle East Respiratory Syndrome Collaboration Barriers
Dr. Munster’s Research Public Health Science Research Bioinformatics Strategic Planning Middle East Respiratory Syndrome (MERS) Big Data Vincent Munster PhD, NIH Infectious Diseases Multiple Sources Isolated Analysis Inaccessibility Integration Challenges Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Collaboration Barriers The platform allows me to analyze and grasp large seemingly incomprehensible datasets. - Vincent Munster, PhD

13 Dr. Munster’s Research Challenges
Private Data PubMed 10,000 Titles Tools FAERS 200 Hours DisGeNet 250,000 Pages Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Knowledge PharmGKB Diseases Articles Collaborators HGNC

14 Diseases Articles Collaborators Our Tested Solution Private Data
PubMed FAERS DisGeNet Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 PharmGKB Diseases Articles Collaborators HGNC

15 Use Case Metamodel Drug Drug Component Chemical Disease Pathway Gene
DrugBank Gene Author CTD PharmGKB Molecular Function PubMed DisGeNet HGNC Biological Process Researcher Datasets Publication PubChem Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Private Datasets Chromosome 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. Cell Component

16 HHS Ignite DEMO

17 Appendix

18 SEMOSS Supplementing Insights
Relevant Data Data Sources Gene Expression Chemical Cellular Pathway Molecular Function Biological Process Cytolocation Cell Component Gene Nomenclature Disease Publication Author Private Research Data Online Mendelian Inheritance in Man (OMIM) PubMed HUGO Gene Nomenclature Committee (HGNC) DrugBank Comparative Toxicogenomics Database (CTD) Disease Gene Network (DisGeNet) PubChem PharmGKB Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015

19 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 Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015

20 SEMOSS maximizes HHS Open Data ROI by leveraging the vast networks of public and private life science data to promote insight and discovery. Solution Overview SEMOSS Solution Scientific Use Case PubMed PharmGKB DisGeNet MESH Federal Health Data Environment HGNC CTD FAERS Cloud Infrastructure Get rid of transitions SEMOSS SEMOSS SEMOSS Platform Which diseases are associated with my genes of interest? End Users Cancer Researcher

21 Solution Demonstration
Data Sources Private Data HGNC OMIM DisGeNet CTD PharmGKB PubMed FAERS VAERS Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Diseases Articles Collaborators

22 Solution Demonstration
Data Sources Private Data HGNC OMIM DisGeNet CTD PharmGKB PubMed FAERS VAERS Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Diseases Articles Collaborators

23 Solution Demonstration
Data Sources Private Data HGNC OMIM DisGeNet CTD PharmGKB PubMed FAERS VAERS Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Diseases Articles Collaborators

24 Solution Demonstration
Data Sources Private Data HGNC OMIM DisGeNet CTD PharmGKB PubMed FAERS VAERS Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 The platform allows me to analyze and grasp large seemingly incomprehensible datasets. - Vincent Munster, PhD Diseases Articles Collaborators

25 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. 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. Find and Import Data Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 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 Special Thanks to… The Team Joe Croghan Project Supervisor NIH
Brock Smith Project Lead NIH Karthik Balakrishnan Technical Lead NIH Alexandra Kwit Science Lead NIH Prabhu Kapaleeswaran Author, SEMOSS MHS Alexander Sherman Technical SME NIH LeeAnn Bailey, PhD Science SME FDA Regina Cox Data SME CDC Special Thanks to… Ask up front, put phases 4, 5, and 6 on this ( March 2015, septbem 2015 Mike Tartakovsky NIH Alex Rosenthal NIH Peter Jahrling, PhD NIH David Parrish NIH Vincent Munster, PhD NIH Dawei Lin, PhD NIH


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