Introduction
The group aims to bring together Turing Fellows who have a research interest in genomics, proteomic, transcriptomic, metabolomic technologies to share best practice in experimental design and develop new methodologies to interpret these data through the application of machine learning, AI and novel computational and mathematical methods.
Explaining the science
Modern omic methods enable the generation of large amount of biological data with ever decreasing cost from a single experiment. However, badly designed experiments can result in worthless data. This interest group aims to ensure that these challenges are resolved before the first experiment has begun.
Aims
With the advent of whole-genome sequencing, single-cell sequencing and the advances in mass spectrometry methods, good experimental design and data analysis methods are more important than ever. Our objective is, therefore, to share best practice at the interface between data sciences and experimental biologists to ensure reproducible science and improve research outcomes. This meets the Turing goal to "advance world-class research and apply it to real-world problems."
Talking points
What are the key challenges in designing a large scale omic experiment?
Challenges: Experiments are easily confounded by other variables, and poor experimental design may prevent answering the key research questions.
Example output: Publication of best practice methods.
How do we interpret machine learning outputs of biological data?
Challenges: Machine learning is incredible powerful; however, in the biomedical contact the results of model may be complex to interpret and implement the results.
Outcome: Strategies for design and interpreting machine learning strategies for real world impact.
How to get involved
Organisers
Dr Andrew Holding
Lecturer in Biomedical Science, University of York
Dr Owen Rackham
Theme Lead in Cell and Molecular MedicineContact info
Andrew Holding
[email protected]
Ben MacArthur
[email protected]
External researchers
Heather Jackson, Imperial College London
Michal Zulcinski, University of Leeds
Laurent Gatto, Université catholique de Louvain
Srijit Seal, University of Cambridge
Vasiliki Rahimzadeh, Stanford University
Fotios Drenos, Brunel University London
Zhi Yao, Lifearc
Atif Khan, Newcastle University (WCMR, CDT for Big Data & Newcastle AI Lab)
Jack Kelly, University of Manchester
Konstantinos Thalassinos, University College London
Rob Ewing, University of Southampton
Anas Rana, University of Birmingham
Becki Green, King’s College London
Nazanin Kermani, Imperial College London
Nathan Skene, Imperial College London
Sarvesh Nikumbh, Imperial College London & MRC London Institute of Medical Sciences
Jean Baptiste Cazier, University of Birmingham
Brian Schilder, UK Dementia Research Institute at Imperial College London
Fotios Drenos, Brunel University London
Andrew Owen, University of Birmingham
Aladdin Ayesh, De Montfort University, Leicester
James Timmons, William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London