Day 1 November 3

BioData EU 2017 Day 2

Jo Churchill
08:55

Chairman’s remarks and recap of Day 1

KEYNOTE: Implementing Big Data Across Healthcare Systems

Keith McNeil
09:00

Keynote Interview – Healthcare & Data: Implementing the NHS patient “data lake”

Panel discussion
09:40

Panel: Data Security & Protection: Where is the patient in all of this?

  • How do you engage people? What innovative approaches can be used to engage the public? Social media? Lessons learnt from social services and education outreach programmes.
  • How do diverse communities feel about sharing their data?
  • What is realistic in terms of patients’ knowledge of how their medical data is being used in research?
  • Discussions around ‘consent’ or ‘opt-out’

10:20 Networking refreshment break

Data Storage & Management

BioData of the Future

Transforming Big Data into Smart Data

Population & Public Health

Data Security & Protection

Innovations in BioData

Cancer

BioData and Epidemiology

11:05

Governance requirements for electronic health records and reuse for research

  • Ethical, privacy protection and governance requirements for electronic health record architectures and systems
  • What challenges to this privacy protection does research use pose?
  • How can clinical research make use of interoperable electronic health records in a trustworthy way?
11:05

Setting the computational standard for scalable genomics and personalised medicine analysis

  • Gain insight into the work being done to create a standard computational method for HPC at a time when genomics data is growing
  • What hurdles were met during development?
  • What opportunities does this standard present to the biodata sector?
11:05

Case Study: Translational bioinformatics in oncology

  • What translational bioinformatics is in an academic and multidisciplinary environment within the field of oncology
  • What are some of the strategies, issues and bottlenecks encountered during recent studies?
  • Review a platform developed for NGS automated data management
11:05

Understanding disease outcomes with biodata

  • Gain an understanding of how biodata is assisting epidemiology studies within drug development and equally drug failures
  • What are some of the challenges around availability of data when looking at real world evidence and disease outcomes?
  • How have population centric phenotypic data helped within this approach?
11:25

Data privacy requirements in pharma

  • Review challenges faced by pharma companies in data privacy
  • How do you continue to push scientific development and innovation whilst maintaining adequate data protection measures?
  • How does data privacy sit with the pharma business strategy as a whole?
11:25

How artificial intelligence is innovating drug discovery and development

  • Gain an understanding of how medical research is seeing rapid transformation driven by an explosion in data and advances in AI
  • How is BenevolentAI’s approach, of identifying and developing novel products and novel uses of existing products, taking advantage of existing data sets, and the need to access and re-evaluate pharmaceutical databases?
  • What are the main benefits of this AI-based approach to a drug discovery pipeline: time, attrition, financial, and for patients?
11:25

Biodata and research into brain tumours

  • Is big data an opportunity or challenge for research into brain tumours?
  • How can those affected by brain tumours lead us on use of their data?
  • How can biobanking help advance developments within this space?
11:25

Link between population-level epidemiology and genetic data

  • What can you learn from linking population-level EHRs and genetic data?
  • How can you use other technologies to better understand a disease, such as wearables and other data systems, to track and manage people over time?
  • How do you overcome the challenge of finding the longitudinal phenotypic data required for these studies?
11:45

Prediction of novel therapeutic targets using an innovative data approach

  • Gain a better understanding of the challenges around target identification and validation
  • Review a semi-supervised classification approach to explore whether gene – disease association data is sufficient to predict therapeutic targets 
  • Investigate how a neural network is able to predict therapeutic targets with over 70% accuracy demonstrating that disease association is predictive of the ability of a gene or a protein to work as a drug target
11:45

Integrating knowledge biocuration with computational biology in cancer

  • Hear an overview of the biocuration-based activities being undertaken by the SIB Swiss Institute of Bioinformatics in the field of cancer
  • Best practices for transforming big data into smart data within this research and development field
  • Gain insights into biota analytics for drug repurposing in immunotherapy
11:45

Microbial bioinformatics and epidemiology

  • Gain an understanding of advancements in software development and data analysis within the field of microbial bioinformatics
  • How is biodata specifically working towards combatting anti-microbial resistance?
  • What new methods and technologies are being developed within the microbial bioinformatics space?

12:25 Networking lunch break

13:30

Data Storage & Management

BioData of the Future

Transforming Big Data into Smart Data

Population & Public Health

Successful Strategies for R&D Data Management

Applications of AI & Machine Learning with BioData

BioData for Health Systems

BioData for Population and Public Health Applications

13:45

Integration of RWE data in the evaluation of clinical probability of success via clinical trial simulation

  • Best practices in modelling to incorporate different data sets
  • Hear a case study of this application within diabetes
  • Examine how to create an Integrated Data
13:45

AI methodologies and optimisation within childhood cancer

  • Insights into big data analytics on the causation of a rare childhood cancer
  • How is this enabling drug discovery for this particular patient cohort?
  • Investigate methods used to identify drug responses from each patient within that same cohort
13:45

Lessons learnt from changing clinical pathways to embed genomic testing

  • How do we work with hospitals to embed genomic testing in clinical pathways where appropriate?
  • What tools are needed by healthcare to adopt?
  • What is the clinical impact of this change, including impact on cost and value?
13:45

The role of bioinformatics in fighting infectious diseases

  • Hear how bioinformatics tools are being used for pathogen identification and typing, as well as pathogenicity and virulence within infectious diseases
  • How is this research helping to identify and combat antimicrobial resistance?
  • Gain an understanding of applications for bioinformatics within infectious diseases: drug resistance testing, pathogen-host interaction, infection and treatment outcomes
14:05

“How to eat the elephant”: A review of information architecture

  • How do you make information architecture a priority in corporate environments and deliver immediate impact?
  • Insights into a structured approach to overcome the core challenges of large enterprises to build key data assets
  • Best practices in applying this to a pharmaceutical R&D business
14:05

Artificial intelligence & machine learning applications in clinical development: Present & future

  • Gain insights into machine learning as applied to the intelligent mining of clinical trials data and examine predictive analytics to refine the operational execution of drug development
  • What are some of the challenges & opportunities presented by the burgeoning role of technology in clinical research?
  • Consider the future: How AI might be used in years to come?
14:05

Case Study: Connected Health Cities and the ‘Learning Health System’

  • Explore how this project unites health data and technology to improve health services in Northern England
  • Review how applications of advanced data analytics have provided more efficient processes and improved public health to patients
  • What are some of the lessons learnt that can be applied elsewhere in the UK and the rest of the world?
14:05

"Predictive Prevention"

  • Learn about how the Data for Good Foundation is collecting patient data for research on top of personal data storage
  • What is the dynamic around data ownership and governance?
  • How are the foundation interacting with both health and behaviour data?
14:25

Addressing the pain points of bioinformatics R&D in the post-genomic era

  • Lessons learnt from the pharmaceutical industry
  • Focus on data and metadata management
  • Gain an understanding of what’s next for bioinformatics R&D
14:25

Machine learning for medical image analysis

  • Hear how machine learning techniques are being applied to the automatic interpretation of medical imaging
  • What are the benefits of using these techniques within the radiology setting?
  • Where else could these algorithms be applied?
14:25

Using AI to diagnose rare diseases

  • Hear an overview of how AI is helping review patient records and medical literature to generate a series of ranked diagnoses
  • What have been some of the lessons learnt and challenges overcome?
  • Does working with rare diseases alter data protocols and practices at all?
14:25

Using biodata to understand the causes of disease as a basis of disease prevention

  • Explore how population-based studies are enabling strategies for prevention
  • Investigate methodologies that can be applied to biobanks and population-based cohorts when studying disease prevention
  • Best practices in overcoming challenges with global coverage and international collaborations
14:45

How to manage data in a more structured way

  • Update into Roche’s data management and workflow management strategy
  • What have been some of the challenges rolling it out?
  • How has been accepted by the scientists using the platform?
14:45

Novel feature selection strategies for enhanced predictive modelling and deep learning in the biosciences

  • Gain an understanding of how WuXi NextCODE’s robust AI method is helping advance personalised medicine
  • How do their various advanced deepCODE AI tools expand the understanding of the underlying molecular determinants of human cancer?
  • What are these support systems and research tools enabling for the research communities working in this space?

CLOSING KEYNOTE: Horizon Scoping in BioData

Panel discussion
15:10

Panel: The Future of Big Data within Life Sciences

  • Where are we going with biodata? What is the sense of direction?
  • What will be the dynamic between biodata and clinical data?
  • What role will we see alternative sources of data (such as behavioural or environmental data) and new technologies (such as nanotechnology and wearable) play in their interaction with biodata?
  • What is the role of AI and machine learning in all of this?
  • Forward looking perspectives on population-based health studies within biodata. What will the interface be between real world data/ real world evidence and genomics? Will this help to better inform precision medicine moving forward?

15:50 End of Day 2 – See you next year!

last published: 15/Jun/17 16:25 GMT