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The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013
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The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013

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This talk will illustrate the power and flexibility of Graph Databases and Neo4j specifically to help in the overall analysis of biological data sets. Davy will show how to build a visual exploration …

This talk will illustrate the power and flexibility of Graph Databases and Neo4j specifically to help in the overall analysis of biological data sets. Davy will show how to build a visual exploration environment that helps researchers at identifying clusters within various biological data sets, including gene expression and mutation prevalence data. Additionally, he will demo BRAIN (Bio Relations and Intelligence Network), a powerful data exploration platform that combines various scientific data sources (including Pubmed, Swissprot and Drugbank). It uses Neo4J under the cover to both store and enable powerful querying capabilities that provide key insights and deductions.

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  • 1. GraphConnect
  • 2. the power of graphs to analyze biological data
  • 3. about me who am i ... ➡ big data architect @ datablend - continuum provide big data and nosql consultancy • 5 years of hands-on expertise in the pharma/biotech sector • Davy Suvee @DSUVEE
  • 4. big data in pharma massive data scalable number crunching platform complex data visual insights-driven platform full genome sequencing biological networks graphs!!
  • 5. big data in pharma (2 specific use cases) outlier detection platform neo4j, mongodb/cassandra and gephi euretos - brain neo4j, mongodb, solr and prefuse
  • 6. gene expression clustering ➡ oncology data set: ★ 4.800 samples ★ 27.000 genes ➡ Question: ★ for a particular subset of samples, which genes are co-expressed?
  • 7. storing gene expressions (mongodb) { "_id" : { "$oid" : "4f1fb64a1695629dd9d916e3"} ,   "sample_name" : "122551hp133a21.cel" ,   "genomics_id" : 122551 ,   "sample_id" : 343981 ,   "donor_id" : 143981 ,   "sample_type" : "Tissue" ,   "sample_site" : "Ascending colon" ,   "pathology_category" : "MALIGNANT" ,   "pathology_morphology" : "Adenocarcinoma" ,   "pathology_type" : "Primary malignant neoplasm of colon" ,   "primary_site" : "Colon" ,   "expressions" : [ { "gene" : "X1_at" , "expression" : 5.54217719084415} ,                     { "gene" : "X10_at" , "expression" : 3.92335121981739} ,                     { "gene" : "X100_at" , "expression" : 7.81638155662255} ,                     { "gene" : "X1000_at" , "expression" : 5.44318512260619} ,                      … ] }
  • 8. correlating samples (mongodb/map-reduce) x pearson correlation y 43 99 21 65 25 79 42 75 57 87 59 81 0,52
  • 9. co-expression graph (neo4j) 122551 correlat ed 6 create an edge between both nodes 8 value : 0, ➡ create a node for each sample ➡ if correlation between two samples >= 0.8 122553 122552
  • 10. co-expression visualisation (gephi)
  • 11. euretos - brain ➡ pubmed: 23 million biomedical articles 1300 new ones added every day • google-like search interface • ➡ reading an article ... • malaria is transferred by mosquitoes
  • 12. euretos - brain authors references
  • 13. euretos - brain ooooooh crap ...
  • 14. euretos - brain ➡ nanopub (nanopub.org) • the smallest unit of publishable information ➡ assertion • subject: malaria • predicate: transferred by • object: mosquito ➡ provenance • how this came to be (meta-data)
  • 15. euretos - brain ➡ unfortunately, malaria is encoded in various ways ... db1 db2 db3 malaria P22384 AQ879 malaria
  • 16. euretos - brain malaria transferred by mosquito
  • 17. euretos - brain ➡ brain (http://www.euretos.com/brain) exploration and analysis platform • millions of concepts/triples/nanopubs • pubmed, uniprot, omim, pubchem, ... • ➡ architectural stack • • • meta-data is stored in mongodb graph in neo4j swing interface connecting to rest endpoints
  • 18. brain
  • 19. brain
  • 20. brain
  • 21. brain
  • 22. brain
  • 23. brain
  • 24. brain
  • 25. brain
  • 26. Questions?
  • 27. datablend - continuum Follow us E-MAIL twitter.com/data_blend www.datablend.be info@datablend.be www.datablend.be info@datablend.be 0499/05.00.89

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