GENOMICS HACKATHON

 

28 November 14:00-18:25

 

Overview

Participants in the hackathon will be introduced to several genomic and gene-disease data sources.  Participants will explore methods for using these data for predicting drug discovery and drug development success.

Participant background

Suitable for participants with some form of data science background, with some capability of organizing, graphing, analyzing, and/or building sophisticated prediction models.

Format

Participants will be divided into teams of 4-6 each, with a team leader assigned and prepared for guiding the team through the exercise beforehand.  To be most effective, hackathon participants should sign up >1 week ahead of time with a background survey to permit creation of suitable teams with realistic team objectives to be clearly articulated.  Teams will work for up to 3 hours on their respective challenges.  At the conclusion, teams will present their results to the broader group.  Example team challenges could include the following:

  • Propose new ways of interacting with the data through OT v2.0; better data interactions (data science skill level ≥ 1)
  • Explore relationships between UK Biobank association results (>3000 GWAS) with target success (data science skill level ≥ 2)
  • Characterize the contribution of individual factors to likelihood of reaching clinical trial (data science skill level ≥ 2)
  • Characterize the contribution of individual factors to likelihood of achieving clinical trial success (data science skill level ≥ 2)
 
 

 

Genomics Hackathon

Matt Nelson
11:40

Genomics Hackathon

DATATHON ON OPEN TARGETS: In partnership with EMBL- EBI & GSKPart 1: Discoveringthe open targets platformOpen Targets is an innovative, large-scale, multi-year, public-private partnership that uses human genetics and genomics data for systematic drug target identification and prioritisation.Visit theOpen Targets Platformwhich provides an integration of public domain data to enable target identification and prioritisation.Generating and interpreting the data required to identify a good drug target demands a diverse set of skills, backgrounds, evidence types and technologies, which do not exist today in any single entity. Open Targets brings together expertise from four complementary institutions to systematically identify and prioritise targets from which safe and effective medicines can be developed.Our goals are to:
  • Systematically find the best targets to safely & effectively treat disease
  • Help others find good targets
  • Get those targets adopted into drug discovery pipelines
We currently focus on oncology, immunology and neurodegeneration through an R&D framework that can be applied to all aspects of human diseaseHackathon
  • Overview: Participants in the hackathon will be introduced to several genomic and gene-disease data sources. Participants will explore methods for using these data for predicting drug discovery and drug development success.
  • Participant background: Suitable for participants with some form of data science background, with some capability of organizing, graphing, analyzing, and/or building sophisticated prediction models.
  • Format: Participants will be divided into teams of 4-6 each, with a team leader assigned and prepared for guiding the team through the exercise beforehand. To be most effective, hackathon participants should sign up >1 week ahead of time with a background survey to permit creation of suitable teams with realistic team objectives to be clearly articulated. Teams will work for up to 3 hours on their respective challenges. At the conclusion, teams will present their results to the broader group. Example team challenges could include the following:
    • Propose new ways of interacting with the data through OT v2.0; better data interactions (data science skill level ≥ 1)
    • Explore relationships between UK Biobank association results (>3000 GWAS) with target success (data science skill level ≥ 2)
    • Characterize the contribution of individual factors to likelihood of reaching clinical trial (data science skill level ≥ 2)
    • Characterize the contribution of individual factors to likelihood of achieving clinical trial success (data science skill level ≥ 2)
last published: 15/Nov/18 18:25 GMT

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Contact us

To sponsor or exhibit contact:
Alistair Wilmot
+44 (0)207 092 1174

alistair.wilmot@terrapinn.com


To speak:
Chris Shanks
+44 (0)207 092 1151

chris.shanks@terrapinn.com