Day 1 November 2

BioData EU 2017 Day 1

KEYNOTE: Implementing Big Data Across Life Sciences R&D and Drug Discovery

One-on-one interviews with some of biodata’s principal thought-leaders, discussing what they are doing to enable the implementation of big data across in their R&D pipelines and within healthcare practices
Ruth March

Update on AstraZeneca’s Integrated Genomics Initiative

Mauricio Carneiro

Experimental and computational platforms at Verily Life Sciences


Question and answer session


Speed Networking

10:20 Networking refreshment break

Pedro Ballester

Building more predictive and interpretable in silico models of in vivo drug response via model complexity control

  • Hear an overview of how the high dimensionality of pharmacogenomics data hampers the discovery of complex molecular markers
  • Gain an understanding of ways to improve the performance of machine learning algorithms on high-dimensional data in the context of precision oncology
  • Examine how to assess which molecular profiles are most predictive of PDX response depending on treatment and cancer type
Andy Grant

13:00 Networking lunch break

Data Storage & Management

Data Computing & Processing

Transforming Big Data into Smart Data

Data Sources

Data Integration & Interoperability

Up in the Cloud

Rare Diseases

Innovations in Data Sources and Acquisition Technologies


Integrating proteomics in precision medicine

  • Gain an understanding of the role proteomics is playing in precision medicine, and their benefits beyond genomics
  • Best practices in integrating multi-omics data
  • Explore the latest developments within mass spectrometry methods

Case Study: Federated In-Memory Database System (FIMDB)

  • Overview of this platform that boasts the benefits of a public cloud but with private processing capabilities
  • Explore the technical foundations of this platform that combines integrated data processing and real-time data analysis
  • In what way was there unmet need for such a managed service within academia internationally?

Collecting and storing standardised clinical data within rare diseases

  • How has working within rare diseases forced a more progressive approach to biodata?
  • What role has automation played throughout the rare disease analysis pipeline?
  • What are some of the challenges integrating genomic and clinical data from a standardisation perspective?

Standardising data capture

  • How does the biodata community need to come together to standardise the language used in data capture?
  • How does this differ between genomic and clinical data?
  • What role does incentivising data generators play in the standardisation of the data captured?

Infrastructure development for Europe’s human genomics data

  • Gain an understanding of human data discovery through ELIXIR Beacon project
  • Challenges in managing human genomics data in rare diseases and common complex diseases - working across different countries and multiple institutions
  • How can this infrastructure enable new insights leading to improved healthcare outcomes?

Lessons learnt from implementing a cloud initiative in pharma

  • What have been some of the major bottlenecks encountered when moving into the cloud?
  • Best practices in engaging the research side with the IT side of a pharma business to ensure a successful transition into the cloud
  • How do you best consider the business strategy in line with the IT agenda in order to enable and elevate the pharma business to its greatest potential?

The role of data in drug repurposing for rare diseases

  • Delve into the role data has played in the ability to repurpose existing drugs for treating rare diseases
  • How does machine learning along with biodata analysis enable these drugs to be matched with diseases?
  • What’s next for this approach?

Data capture methods for long-term robustness and authentication

  • A model to enable anonymous aggregated analysis of federated data
  • A strategy for long-term data management
  • Authentication using Blockchain for data capture & other pipeline processes 

Genomic data sharing and integration for clinical applications

  • Insights into drivers and barriers to genomic data sharing and integration in the NHS
  • What is the clinical necessity?
  • What are some of the ethical and legal dimensions?

Using the cloud in drug discovery and design

  • Examine how the cloud is being used to computationally design new drugs
  • How does this method differ from more traditional drug discovery methods and what are the benefits?
  • What does the future look like for the combination of AI and cloud computing in the drug discovery space?

Biobanks as a data resource

  • Hear an overview of the current status of biobanking and how they consume data
  • What are some of the challenges around getting clinical sample data, a different kind of big data?
  • What role do biobanks play with regards to data linkage and anonymization?

The Automated Data Scientist

  • Gain an understanding of ways to curate and weave complex data types to make them useful for analysis?
  • What have been some of the major challenges to overcome when working with biological, clinical and RWE data sets?
  • What role does automation play in data curation?

Faster Results to Discovery: Adding High-Speed Data Transfer to your Cloud Applications

  • Moving big data to the cloud for collaborative research and pharma applications is nearly impossible over the internet and shipping hard drives is a risky alternative
  • Learn how IBM Aspera high-speed transfer software makes it possible securely move data of any size to the cloud at speeds 100s of times faster than other solutions
  • Explore best practices from organizations like BGI and Bluebee High Performance Genomics that use Aspera to accelerate critical cloud-based research and bioinformatics
Per Hansen, Regional Manager Sales Engineering, Aspera, an IBM company

The role of biodata from rare disease discovery to clinical application

  • How is the growth in precision medicine and biodata helping to accelerate progress in the development of treatment for rare diseases?
  • Where are there still pitfalls in rare disease data that can be addressed?
  • What is the biggest opportunity moving forward for biodata and rare diseases?

Cognitive Search & Analytics: Building a next-generation search platform for biodata to

  • Overcome the challenges of working with large, diverse datasets, of both structured and unstructured, from internal and external sources within pharmaceutical R&D
  • Enable Pharma companies accelerate research and shorten Drug Time-to-Market
  • Drive innovation within research whilst maintaining information governance and security 
Dominique Raimbaux, UK Regional Sales Manager, Sinequa

15:40 Networking refreshment break

Data Storage & Management

Data Computing & Processing

Transforming Big Data into Smart Data

Open-Source Data Models

NGS & NGS Informatics

Other Therapeutic Areas


Case Study: Open Targets

  • Hear about the new data being generated as part of the Open Targets public-private initiative
  • Update on the bioinformatics platform
  • What is the progress being made in experimental data and how are they integrating that data into the platform?

Highly scalable NGS data processing between research and clinic

  • Gain an understanding of how the QuickNGS and CancerSysDB platforms achieve highest throughput in NGS applications, ranging from basic research to clinical investigation
  • Hear how this highly scalable approach can crunch large-scale study population data and facilitates timely analyses of patients' personal genomes
  • Review challenges including the appropriate deal with national rules for personal data protection, uninterrupted availability of HPC resources, and maintenance of highly complex analysis codes

Renal virtual clinics and online monitoring of kidney disease

  • Updates on collecting data from patients doing dialysis at home and from a virtual clinic
  • Lessons learnt from this format of data collection that can be applied across other therapeutic areas
  • Explore clinical benefits being seen as a result of this data

Data-sharing across borders: A Nordic case study

  • What are some of the ethical, legal and technical issues encountered in sharing data across Nordic borders, and what are possible solutions?
  • Lessons learnt from both the Nordic Network and the BigMed Project that can be applied in other geographies
  • Exploring the value of the third party role in data sharing

Adoption of NGS in the Mayo Clinic

  • Insights from a leading clinic as to where NGS is going within healthcare
  • Review of MP-Seq as a powerful clinical tool
  • With all the tools Mayo Clinic has, why are they not doing genomes?

On Biomarker Discovery

  • Analysis roadmap
  • Systematic validation of early findings
  • Biomarker validation approaches 

How to analyse large virtual cohort of distributed Biomedical data

  • Access and search petabytes of public and private data sets
  • Build a desired cohort using multiple data sources with different custodians, access rules, and locations
  • Project data in the form expected by your analysis application
  • Run analytics anywhere with apps and data that are interoperable and technology independent
Les Mara, Founder, Databiology

Benefits of whole genome cohort migration to GRCh38

  • What are the key considerations around performance, storage and quality?
  • Review best practices in working at scale with large cohorts of whole genome data
  • What yield can you expect when using GRCh38?

Case Study: Moorfields Eye Hospital – DeepMind Health Collaboration

  • Hear an overview of the collaboration where they are training an algorithm to diagnose retinal diseases
  • What have been some of the challenges to overcome within this particular project?
  • What are the biggest opportunities on offer by using AI technology within ophthalmology?

Realising the potential of big data through multi-national open-source data sharing

  • Update on the work EMBL-EBI are undertaking
  • Why is it important to have multi-national open-source data sharing platforms within biodata?
  • What are some of the implementation challenges around national rules and regulations, governance and project funding?

Project MinE: an international whole-genome sequencing study (22’500 samples) in amyotrophic lateral sclerosis

  • What is ALS/MND? A brief introduction to this devastating disease and its genetics 
  • What is Project MinE? Consortium design, data infrastructure and analysis 
  • Preliminary results, future prospectives and role in both the international genetic research and population health
Alfredo Iacoangeli, postdoctoral researcher, King's College London

17:45 End of Day 1 – Networking Drinks Reception

last published: 19/Oct/17 15:05 GMT