Modeling and mining large scale biological seman0c networks using NEO4J

Transcription

1 Modeling and mining large scale biological seman0c networks using NEO4J Junaid Gamieldien Principal Inves.gator Clinical Sequencing and Biomarker Discovery

2 Neo4J Graph database Graph is composed of two elements: a node and a rela.onship. nodes (ver<ces) represent an en<<es (person, place, gene, protein, expression value) rela0onships model how two nodes are associated, e.g. breast cancer and cancer could have the rela<onship type_of poin<ng from breast cancer to cancer Rela<onships are first class ci.zens Simpler data models and more expressive (beher seman<cs) connected nodes physically point to each other in the database

3 Whiteboard friendly Brainstorming almost always involve drawing connec<ons between elements => natural and intui0ve data model Forcing that model into a a tabular framework creates a mental disconnect with primary model => querying becomes extremely difficult Developers suffer because the tabular model does not match their mental model of the applica<on In neo4j the whiteboard sketch is the database model

4 Alice Friend Of Friend Of Bob Graph Databases Book

5 name: Alice age: 38 HAS_READ on: 10/03/2013 rating: 5 FRIEND_OF since: 07/09/2011 FRIEND_OF since: 07/09/2011 name: Bob age: 34 HAS_READ on: 03/02/2013 rating: 4 title: Graph Databases authors: Ian Robinson, Jim Webber

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7 Tom Hanks ACTED_IN Hugo Weaving ACTED_IN ACTED_IN Cloud Atlas The Matrix DIRECTED DIRECTED Lana Wachowski

8 name: Tom Hanks nationality: USA won: Oscar, Emmy name: Hugo Weaving nationality: Australia won: MTV Movie Award ACTED_IN role: Zachry ACTED_IN role: Bill Smoke ACTED_IN role: Agent Smith title: Cloud Atlas genre: drama, sci-fi title: The Matrix genre: sci-fi DIRECTED DIRECTED name: Lana Wachowski nationality: USA won: Razzie, Hugo

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10 Property Graph

11 Searching by TRAVERSAL start n=(people-index, name, Andreas ) match (n)--()--(foaf) return foaf n

12 neo4j u0lity Modeling data with a high number of data rela<onships Flexibly expanding the model to add new data and/or data rela<onships Querying data rela<onships in real- <me Knowledge representa<on

13 More than just ease of modeling: SPEED

14 Technical ahrac<ons for Biology Schema- free labeled Property Graph Perfect for complex, highly connected data Scalable: Billions of Nodes and Rela<onships Fast: > 2Million traversals / second Server with HTTP API OR Embeddable on JVM Declara<ve Query Language (CYPHER)

15 Bioinforma0cs (biomedical) example

16 BIG DATA + EXISTING KNOWLEDGE (OR MULTIPLE INTEGRATED SOURCES OF EXISTING KNOWLEDGE) + A GOOD QUESTION (OR EVEN A HUNCH) = NEW KNOWLEDGE/LEADS/ANSWERS

17 Challenges Cross knowledge- domain integra<on Knowledge representa<on Simplifying complex querying Evidence based automated discovery through logical deduc.on?

18 Disease Gene Candidate Priori0za0on Typical ques<ons bioinforma<cists ask (or should) - is the gene: known to be involved the disease? (easy) involved in related disease? (mostly overlooked) with a func<on that coincides with the disease pathology, biochemistry, etc? (not easy) in a disease- associated pathway? (not easy) Too many candidates + too many resources to humanly interrogate leads to excessive pre- filtering!

19 BORG (BioOntological Rela<onship Graph) Database that models millions of biomedical facts the way humans understand them Enables logical querying across all relevant facts the way a researcher would Reports relevant results along with the evidence and with meaning

20 Differen<a<ng features of the tech Graph database (rela<onal databases cannot cope with network models) Knowledge rather than data modeling Enables transi.ve associa<on Able to instantly assimilate an en<re new knowledge domain and then know it and ask ques<ons across it

21 And we can teach it about a disease self- building!!

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23 Candidate Discovery?

24 Future?

25 How do I get data into neo4j? 1. Scribble down a realis0c model (napkin is OK) 2. Insert nodes along with their proper<es 3. Insert rela<onships along with their proper<es (forward and reverse, if that is semanbcally correct) 4. DONE J Wrappers exist for virtually any programming language

26 Thank you