BioData EU 2018 - Agenda Day 1

 

BioData EU- Day 1

IT, DATA MANAGEMENT AND STORAGE

  • Pooling of internal and external data sources for clinical development
  • Overview on business use cases at Novartis, and underlying technology platform(s)
  • Critical enablers of the journey (e.g. agile governance, partner ecosystem, rapid data prototyping)
08:55

Opening Remarks

Abel Archundia
09:00

Keynote opening address

Bertrand Bodson
09:40

Reimagining medicine with data and digital

Marco Marsella
10:00

Enabling healthcare and precision medicine with AI

10:20

Speed Networking

10:40

Morning Refreshments

round tables
11:40

Round Tables

Round Table A: Biomarker discovery in the world of AI
Enrico Ferrero

Enrico Ferrero, Translational Bioinformatics Team Leader, GSK

Round Table B: Using Electronic Medical Record (EMR) data to inform big pharma’s drug discovery, development, and marketing processes
Nicholas Kelley

Nicholas Kelley, Research Investigator, Genomics, Novartis Institutes

Round Table C: Agile Data Management – Challenges, emerging practices and way forward
Round Table E: How to improve efficiency in machine learning producibility- Western Digital
Linda Zhou

Linda Zhou, Director, HPC and Life Sciences Solutions, Western Digital

Round Table F: Accelerating Innovation in Pharmaceuticals
Pierre-Mikael Legris

Pierre-Mikael Legris, Co-founder and CEO, Pryv

Round Table H: Sequencing and collaboration
David Lloyd

David Lloyd, Project Technical Coordinator, ELIXIR

Round Table I: Using digital technologies in clinical trials
Atar Libovitch

Atar Libovitch, Head of Business Solutions, Teva Pharmaceuticals Industries Ltd

Round Table K: Examining developments on data privacy
Maria Chiara Atzori

Maria Chiara Atzori, Head Data Privacy Switzerland, Novartis

Round Table L: Learnings and results from large-scale investments in data across the pharmaceutical R&D chain
Greg Temesi

Greg Temesi, Director, R, MSD

Round Table M: Standards for clinical trial operational data sharing across the industry
Davide Franco

Davide Franco, Director, Strategy and Operations, Predictive Analytics and Design, Novartis, Novartis

Round Table N: Translational Oncology Informatics
Ronghua Chen

Ronghua Chen, Director of Scientific Informatics, R&D IT, MSD

Round Table O: Examining device-based data capture and analytics
Steffen Roellinger

Steffen Roellinger, SVP – Head Portfolio & Operations of Clinical Operations, Pharma R&D, Bayer

Round Table Q: Artificial Intelligence and Deep Learning in Healthcare and Life Sciences
Wolfgang Mertz

Wolfgang Mertz, CTO, EMEA for Life Sciences and Healthcare, Emerging Technology Division, EMC

Round Table R: Is legacy IT preventing you from making the next drug discovery?
Bill Fox

Bill Fox, Chief Strategist, Mark Logic

Round Table T: Sharing clinical data
Xose Fernandez

Xose Fernandez, Chief Data Officer, Institut Curie

Round Table U: Wellcome Roundtable:Unlocking the Potential of Data to Improve Health for Everyone -What should Wellcome do about Data?
Nick Scott-Ram

Nick Scott-Ram, VP of Strategic and Commercial Development, Sensyne Health

Round Table V: Applications of RNA-seq data to drug discovery
Shanrong Zhao

Shanrong Zhao, Director, Computational Biology and Bioinformatics,Worldwide R&D, Pfizer

Round Table W: Data Science at J&J
Joseph Lehar

Joseph Lehar, VP Data Science, Oncology, J&J, Janssen Pharmaceuticals

Round Table X: Challenges affecting the Implementation of E-Health at Ministry of Health in Gambia
Musa Suso

Musa Suso, Information, Communication Officer, Ministry Of Health & Social Welfare

Round Table Z: Data standards, ontologies and controlled vocabularies
Peter Groenen

Peter Groenen, Head Of Translational Science, Idorsia

Round Table ZA: Digital Clinical Trials ...... moving to the connected future for patients, study investigators and CROs’
Liz Nevin

Liz Nevin, Senior IT Business Relationship Manager, Digital Therapeutics, AstraZeneca

Round Table ZB: Developing data/analysis commons in translational medicine
Jake Chen

Jake Chen, Chief Bioinformatics Officer, University of Alabama at Birmingham

12:30

Networking Lunch

IT, DATA MANAGEMENT AND STORAGE

BIOINFORMATICS

AI, AUTOMATION AND MACHINE LEARNING

BIG DATA IN ONCOLOGY

DATA INTEGRATION & INFRASTRUCTURE

Track 5- DATA INTEGRATION & INFRASTRUCTURE/ BIG DATA IN RARE DISEASES
13:30

Chairs Opening Remarks

Track 1- IT, DATA MANAGEMENT AND STORAGE
13:35

How to make data accessible?

  • Pooling of internal and external data sources for clinical development
  • Overview on business use cases at Novartis, and underlying technology platform(s)
  • Critical enablers of the journey (e.g. agile governance, partner ecosystem, rapid data prototyping)
Track 3- AI, AUTOMATION AND MACHINE LEARNING
13:35

Ensemble Computational Intelligence Reveals Novel Molecular Signatures of Cancer Biology and Pan-Cancer Survival

  • Novel feature learning approaches that enhance quantitative assessment of annotated tissues from The Cancer Genome Atlas.
  • We demonstrate the utility of collapsing molecular signals, from five different -omics platforms, into integrated metagenes that are highly informative across roughly 8,200 tumors, encompassing 22 cancer types.
  • Our a priori biological-knowledge and data-driven network-based approaches improve performance and interpretability of both deep learning and probabilistic programming strategies, revealing novel network-driver genes underlying cancer type specific etiology and pan-cancer patient survival.
Track 1- IT, DATA MANAGEMENT AND STORAGE
13:55

Optimizing Big Data Workloads to Accelerate Drug Development

  • Pharmaceutical companies are saddled with legacy systems containing heterogeneous and disparate data. Increasing the ability to share data requires rationalizing and connecting these systems.
  • IT-enabled portfolio management allows data-driven decisions to be made quickly and seamlessly.
  • Responsive, agile, and flexible platform that can leverage a cloud based approach
Track 2- BIOINFORMATICS & GENOMIC TOOLS
13:55

Techniques from Small Molecule Discovery

  • Data diversity, volume and complexity represent challenges for biologics drug discovery
  • Small molecule discovery utilizes advanced analytical techniques such as QSAR, matched-molecular pairs, etc., to predict relationships between structure and activity
  • This talk will discuss adaptation of these techniques to biologics discovery, highlighting successes and potential pitfalls
Track 3- AI, AUTOMATION AND MACHINE LEARNING
13:55

Using AI to support generation of RWE

Chris Boone, Vice President, Real World Data and Analytics, Pfizer
Track 4- BIG DATA IN ONCOLOGY
13:55

Large-scale biomedical data integration using the IndivuType database

  • IndivuType is a large-scale multi-OMICS data resource based on specimens obtained from a world-wide network of clinical partner sites.
  • All specimens and data are processed according to global standard operating procedures and annotated with high-quality clinical data.
  • A powerful analysis toolbox makes the data accessible for drug target and biomarker discovery, study cohort design as well as basic and health care research.
Track 5- DATA INTEGRATION & INFRASTRUCTURE/ BIG DATA IN RARE DISEASES
13:55

Using RWE to Build Dynamic Cohorts in Real Time

  • Using all your data, regardless of source, to build dynamic cohorts
  • Building a next gen platform to break through data and business silos
  • Building a platform that can adapt to changing regulations and support developing RWE based drug approval processes
Track 1- IT, DATA MANAGEMENT AND STORAGE
14:15

Be Data Ready: the story of Sanofi’s translational data governance

  • What is data governance ?
  • Understanding your data ecosystem: key drivers and enablers
  • The ‘How to’ implementation challenge: a mix of top-down and bottom-up approaches
  • Future vision and perspectives
Track 3- AI, AUTOMATION AND MACHINE LEARNING
14:15

Leveraging Machine Learning and Advanced Analytics in Healthcare and Life-Science: Opportunities and Pitfalls

Trends in healthcare are toward more data, more complexity, more decisions, and less time and resource for execution. The only way to be successful in this environment is to leverage a more advanced framework for healthcare analytics. Examples from advanced healthcare providers will be given. The next level of advance analytics will move into the domain of autonomous decision-making, machine learning, and deep learning. There are already aspects in radiology, where the power of advanced analytics has been demonstrated. But new data sources (genomics, images, IoT, raw ECG) will be included and will enhance this power. The appetite of deep learning algorithms for vast amounts of data and the ability to derive intelligence from diverse sets of noisy data allows us to go far beyond previous capabilities in what we used to call advanced analytics. However, to be successful we need to understand the capabilities and limitations of the new technologies. We also need to develop new skill sets in order to harness the power of deep learning to create business value in an enterprise. Analytic platforms are evolving more rapidly now than ever in the history of computing. Innovations across multiple fronts of technology are enabling unheard of advances in analytic sophistication at ever more decreasing costs per unit of computation. However, there is a dangerous tendency to chase “shiny new objects” when deploying analytic platforms.
Track 4- BIG DATA IN ONCOLOGY
14:15

Precision and recall oncology: combining multiple gene alterations for improved identification of drug-sensitive tumours

  • A small proportion of tumours present strong gene-drug response associations that can be used as predictive biomarkers of drug response.
  • Combining multiple gene alterations of the tumours via Machine Learning (ML) often results in better discrimination than that provided by the corresponding single-gene marker.
  • ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive tumours (i.e. they have a higher recall). This suggests that substantially more patients could eventually benefit from effective drug selection by applying this ML methodology to existing clinical pharmacogenomics data sets
Track 5- DATA INTEGRATION & INFRASTRUCTURE/ BIG DATA IN RARE DISEASES
14:15

Liberating evidence from real world data in the 21st Century: what have we learned from IMI EMIF, and where will IMI EHDEN take us?

  • Insights from the IMI European Medical Information Framework project 2013-2018
  • What is proposed for the IMI European Health Data and Evidence Network 2018-2023
  • Building sustainable infrastructure via federated networks, common data models and data ecosystems
14:35

Refreshment break

IT, DATA MANAGEMENT AND STORAGE

BIOINFORMATICS

AI AND MACHINE LEARNING

BIG DATA IN ONCOLOGY

DATA INTEGRATION & INFRASTRUCTURE

Track 1- IT, DATA MANAGEMENT AND STORAGE
14:55

Chairs Opening Remarks

Track 2- BIOINFORMATICS & GENOMIC TOOLS
14:55

Chairs Opening Remarks

Track 3- AI, AUTOMATION AND MACHINE LEARNING
14:55

Chairs Opening Remarks

Track 4- BIG DATA IN ONCOLOGY
14:55

Chairs Opening Remarks

Track 2- BIOINFORMATICS & GENOMIC TOOLS
15:00

The use of RWD and machine learning in reverse translation

  • Reverse translation is gaining more importance in drug discovery to support application of human data for target-indication prioritization and patient selection
  • Real world data can provide additional source of information to help selecting the right target for the right indication
  • Machine learning applied to real world data can provide enhanced characterization of diseases and patient trajectories supporting more robust reverse translation
Track 5- DATA INTEGRATION & INFRASTRUCTURE/ BIG DATA IN RARE DISEASES
15:00

Towards FAIR data: ELIXIR

Track 1- IT, DATA MANAGEMENT AND STORAGE
15:40

Industrial internet platforms and applications for life sciences

Track 2- BIOINFORMATICS & GENOMIC TOOLS
15:40

Managing Biomedical Data and Metadata in Large Scale Collaborations

  • Data Commons and new Population Scale Omics and Imaging Projects are multiplying.
  • We will discuss strategies and solutions to address collaboration in a world devoid of universal standards and a plethora of regulatory frameworks.
  • We will review best practices for establishing sustainable metadata models in a diverse set of real world scenarios.
Track 2- BIOINFORMATICS & GENOMIC TOOLS
15:40

Role of AI in bioinformatics and Drug Discovery

  • Is AI/ML transforming bioinformatics?
  • Do we have the right data?
  • Examples of using Advanced Analytics
Lindsay Edwards, Head Of Digital, Data And Analytics , GSK Respiratory, GlaxSmithKline
Track 4- BIG DATA IN ONCOLOGY
15:40

A look at the HARMONY Alliance: the role of big data for hematology

Michel Van Speybroeck, Director of Data Sciences, Janssen (J&J)
Track 4- BIG DATA IN ONCOLOGY
15:40

Genomics-Driven Oncology Drug Development

  • WuXiNextCODE has developed leading capabilities to support the next-generation of oncology drug development including:
  • Comprehensive sequencing from archival FFPE and fresh tumor material
  • Management, integration and analysis of massive multi-omic genomic and phenotypic datasets
  • Advanced Analytics and AI to reveal new pathways and oncology drug targets
Track 5- DATA INTEGRATION & INFRASTRUCTURE/ BIG DATA IN RARE DISEASES
15:40

Developing the AZ Data Architecture

Track 1- IT, DATA MANAGEMENT AND STORAGE
16:00

CERN Knowledge Transfer: from fundamental research to MedTech

  • CERN: the largest particle physics laboratory in the world
  • CERN’s knowledge transfer mission
  • From CERN technologies and know-how to MedTech
  • Focus on computing applications
Track 3- AI, AUTOMATION AND MACHINE LEARNING
16:00

The Benefits of Bayesian AI

  • Real-world AI systems need to account for uncertainty and reason probabilistically
  • Bayesian methods provide a principled approach to dealing with uncertainty
  • Recent developments in this area will be discussed
Imtiaz Houssain, Computational Scientist, Novartis

Refreshment Break (Drinks to be served)

IT, DATA MANAGEMENT AND STORAGE

BIOINFORMATICS & GENOMIC TOOLS

AI, AUTOMATION AND MACHINE LEARNING

BIG DATA IN ONCOLOGY

BIG DATA IN RARE DISEASES

Track 1- IT, DATA MANAGEMENT AND STORAGE
17:10

Integrating health into your digital life

  • As Healthcare providers, we need to recognize that people who use our services already have a footprint in the digital world, with services, devices and applications which they are comfortable using. Instituting change in this is likely to lead to disengaged users, with fewer beneficial outcomes.
  • The last 20 years have brought with them a tremendous pace of innovation in many industries, and created whole new industries through technology itself. In many cases, these changes have instilled behavior shifts and expectations in users - it is important as healthcare revolutionizes itself to learn from these, to not reinvent the wheel, and accelerate our movement by exploiting benefits and opportunities from other industries.
  • Obtaining meaningful insight on patients is based on a simple reward/effort ratio. In order to minimize the effort of data collection, this must be seamless, transparent and simple from the person’s perspective – integrating with passive monitoring systems and zero-UI systems make data collection easy, while the value of providing actionable, timely and relevant insight to a patient will keep them engaged with the platform and feel like it provides them with value.
Track 2- BIOINFORMATICS & GENOMIC TOOLS
17:10

Integrative Informatics, a semantic approach to integrating data to answer complex scientific questions

  • Developing a cohort explorer for comparison of genotype and phenotype
  • Overview of Astrazeneca/MedImmune strategy for using genomics in drug discovery
  • Partnership with DNAnexus
Mathew Woodwark, Director of Research Bioinformatics, Medimmune Ltd
Track 5- DATA INTEGRATION & INFRASTRUCTURE/ BIG DATA IN RARE DISEASES
17:10

Where are all the rare diseases?

Track 1- IT, DATA MANAGEMENT AND STORAGE
17:30

Simplifying data analysis and management for life science workflows

Vast amounts of data are the ingredients for Deep Learning and Genomics. Managing, accessing and analyzing this data which typically goes into the Petabytes can be a challenge. This presentation explains DellEMC’s solution for Life Sciences that includes all the necessary components out of one hand. It is not only easily deployed and managed in a data center but allows bio scientists to focus on their work without being IT experts.
Track 3- AI, AUTOMATION AND MACHINE LEARNING
17:30

Removing Bottlenecks: Kicking the tires on modern analytics, AI&ML infrastructure

Deriving value from large amounts of data is nothing new and modern infrastructure is making powerful analytics more accessible to businesses but how can the investment in these new tools be maximised ?This session will look at work Pure storage has done with it’s customers to deliver optimal results from complex analytics, AI and Deep Learning platforms.
Track 4- BIG DATA IN ONCOLOGY
17:30

Enabling multidimensional translational data management and analysis for biomarkers discovery and patients stratification

  • in translational medicine, predictive and prognostic quantitative biomarkers have been growing in size, complexity and diversity
  • translational scientists need to be empowered to explore and analyze complex translational datasets
  • PerkinElmer Signals™ Translational empowers domain experts through Spotfire, a rich visual environment, connected to the Cloud
Track 5- DATA INTEGRATION & INFRASTRUCTURE/ BIG DATA IN RARE DISEASES
17:30

Seeding Discovery for Rare Disease Therapeutic Development through Epilepsy

  • Only 5% of Rare Diseases have therapies due in part to the lack of understanding rare disease biology
  • DNA sequencing of Epilepsy patients can aid in treatment clinical decision support and identify new causal genes, gene variants and gene-gene interactions
  • Creation of Deep, Rich, Standardized Genotype to Phenotype Global Data Sets to determine functional biology contributing to epilepsy and the rare diseases represented can result in identification of new molecular targets and seed novel therapeutic development programs within and across rare diseases of similar biology
Track 1- IT, DATA MANAGEMENT AND STORAGE
17:50

Ensuring patient centricity within the digital world

  • Keeping data focussed on unmet patient need.
  • Using digital channels to engage HCPs
  • Gathering patient insights for strategic decisions
Track 2- BIOINFORMATICS & GENOMIC TOOLS
18:10

Bioinformatics as a Service: A National Resource to Support Biomedical Research in QatarMassively parallel single-cell CRISPR sequencing in organoid models

  • Genetic epidemiology and CRISPR screens both still struggle to make highly predictive comments about drug target modifiers (i.e. precision medicine) .
  • Using liver organoid models, while integrating single-cell sequencing with CRISPR, we are dissecting out the cross-talk between metabolic pathways to understand target interactions.
  • The pipeline and novel methods underlying such genetic screens and the types of results bing generated will be presented.
Track 5- DATA INTEGRATION & INFRASTRUCTURE/ BIG DATA IN RARE DISEASES
18:10

The rare Disease play book- Developing an Orphan Disease knowledge Base at Alexion

John Reynders, Vice President, R&D Strategy, Program Management and Data Sciences, Alexion Pharmaceuticals
18:30

Close of Congress

last published: 09/Nov/18 16:45 GMT

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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