Cambridge Centre for
Data-Driven Discovery

Big Data in Medicine: Exemplars and Opportunities in Data Science

Friday, 19 June 2015, 12.00pm to 7.00pm

Location: Cancer Research UK Cambridge Institute

The data generated by medical care and medically relevant research are rapidly becoming bigger and more complex, particularly with the advent of new technologies. Our ability to advance medical care and efficiently translate science into modern medicine is bounded by our capacity to access and process these big data. From human genetics and pathogen genomics to routine clinical documentation, from internal imaging to motion capture, from digital epidemiology to pharmacokinetics, and from treatment pathways to life course assessment, the big Vs of Big Data - volume, variety, velocity and veracity - abound in medicine. Statistical, mathematical, visualisation, and computational approaches, from a wide range of disciplines, as well systems for innovative ICT-based interventions are needed to keep apace of the complexity in Big Data and to advance medicine.

On 19th June 2015 at the Cancer Research UK Cambridge Institute, Cambridge-based researchers from all Schools of the University and local research institutes, the pharmaceutical industry and our funding and commissioning partners met for an afternoon of talks demonstrating methods and opportunities for harnessing Big Data in medicine.

Read the abstracts and selected presentation slides below.

Programme at a glance

12:00	Registration , Lunch & Poster Session
13:00	Opening remarks	Patrick Maxwell , Regius Professor of Physic
13:10-14:50	Session 1: Exemplars	Chairs: Simon Tavaré (CRUK-CI/DAMTP), John Aston (DPMMS)
13:10	Keynote: Statistical challenges in the analysis of genomic data	Sylvia Richardson, Director, MRC Biostatistics Unit
	Building Insight Into Disease and Therapy from Real-World Evidence Using Graphs and Large-Scale Analytics	Nirmal Keshava, AstraZeneca R&D Information
	Undiscovered Scientific Knowledge from Large Unstructured Text Collections in an Era of Big Data	Nigel Collier, Department of Theoretical and Applied Linguistics
	Integrating Chemical and Biological Data for Drug Discovery	Andreas Bender, Centre for Molecular Informatics, Department of Chemistry
	Mapping the information processing pathways of the cortex: challenges and opportunities	Andrew Thwaites, Psychology Dept, Cambridge University & MRC-CBSU
14:50-15:10	Break
15:10-16:50	Session 2: Opportunities	Chairs: Lydia Drumright (Department of Medicine), John Todd (Cambridge Institute for Medical Research)
	Keynote: Clinical Informatics	Afzal Chaudhry, BRC, Chief Clinical Information Officer
	EMBL-EBI Big Data in Medicine Strategy	Paul Flicek, European Bioinformatics Institute
	Smartphones, Big Data, and Psychiatry	Neal Lathia, Computer Laboratory, and Conor Farrington, Cambridge Centre for Health Services Research, Institute of Public Health
	Treatment Pathways in Cancer Data	Brian Shand, National Cancer Registration Service, Public Health England
	High-Content Microscopy and Big Data: Discovering the genes and pathways that control cells in health & disease	Rafael Carazo Salas, Department of Genetics
16:50-17:10	Poster talks
	Mathematical Methods for Automatic Detection and Tracking of Dividing Cancer Cells in Phase Contrast Microscopy	Joana Sarah Grah, Department of Applied Mathematics and Theoretical Physics
	Statistical tools for single cell gene expression analysis	Daphne Ezer, Department of Genetics/Cambridge Systems Biology Centre
	From big data to big model: a probabilistic approach to infer cancer evolution	Ke Yuan, Cancer Research UK Cambridge Institute
17:10	Closing remarks	Keith McNeil, CUH Foundation Trust CEO
17:30-19:00	Drinks & Poster Session

Posters

Sketch-driven Data Analysis	Neil Satra, University of Cambridge Computer Laboratory
Limitations of de-identification: no reason not to share data	Neil Walker, Department of Medical Genetics
Non-negative matrix tri-factorisation with missing values, applied to drug sensitivity prediction	Thomas Alexander Brouwer, Computer Lab, University of Cambridge
Mineotaur: interactive visual analytics for high-content microscopy screens	Balint Antal, Department of Genetics, University of Cambridge
Molecular principles by which gene fusions affect protein interaction networks in cancer	Natasha Latysheva, MRC Laboratory of Molecular Biology
High-dimensional statistical approaches for heterogeneous molecular data in cancer medicine	Frank Dondelinger, MRC Biostatistics Unit
A computational approach to the genetic basis of antigenic change in influenza A	Sarah James, Department of Zoology
Performing large scale conditional analysis in GWAS: How to better exploit summary statistics?	Paul Newcombe, MRC Biostatistics Unit
Empirical Bayes in Genomics: when dimensionality is a blessing	Gwenael G.R. Leday, MRC Biostatistics Unit, Cambridge
Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis in-house HTS Data	Shardul Paricharak, Department of Chemistry
Little Data, Big Health	Wei Wang, Little Data Labs
Baal-ChIP: Allele-specific ChIP-seq analysis from cancer cell lines	Ines de Santiago, CRUK - Cambridge Institute
The potential of hyperspectral imaging in fluorescent contrast enhanced imaging	Anna Siri Luthman, Department of Physics and Cancer Research UK Cambridge Institute
The ContentMine	Jenny Molloy, Cambridge Synthetic Biology SRI, The ContentMine

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Cambridge Centre for Data-Driven Discovery

Big Data in Medicine: Exemplars and Opportunities in Data Science

Programme at a glance

Posters

Cambridge Centre for
Data-Driven Discovery