Machine Learning is being used, to great effect, to enhance the value of medical data in the direct-to-consumer genomics space. The success of 23andMe shows the huge potential of consumer-led genomics analysis for scientific research: they recently used ML to collate 600,000 customers’ personalised genetic evaluations, resulting in uncovering a link between genetics and weight. With over 2m customers, and a recent strategic partnership with GlaxoSmithKline, it is likely that this is only their first step into ML based genomics.

Unlocking biological truths in healthcare data

Machine learning is essential if we are to unlock the true value in diverse, large healthcare datasets. As ML models are helping us discover biological truths, they are themselves improving through inspiration drawn from biology itself. This is perhaps most true in neuroscience, where neural network based deep learning draws inspiration from the workings of the brain, but in turn help us understand how these neurons work together to accomplish complex processes.  As genomics helps us to uncover the complex regulatory processes involved in tumour initiation and growth, it is possible that the same kind of synergistics advances will be made and that genomics may help improve and inspire new deep learning methodologies.

 

Read more about the crossovers of artificial intelligence (AI) and biological studies:

Part 1: HOW DO BIOLOGY AND MACHINE LEARNING INSPIRE ONE ANOTHER?

Part 2: HOW IS MACHINE LEARNING AIDING IN THE UNDERSTANDING OF GENOMICS DATASETS?

Part 3: APPLICATIONS OF MACHINE LEARNING AND GENOMICS IN THE TREATMENT OF CANCER

BIOS

genomics artificial intelligence medicine healthcare AI machine learning

John Cassidy:

John is Co-founder and CEO at Cambridge Cancer Genomics, a precision oncology startup building software solutions for iterative medicine. CCG uses integrated bioinformatic and machine learning pipelines for liquid biopsy analysis, in order to guide doctors on tumour treatment in real time. John is actively involved in the biotech startup community as a Venture Partner at the Pioneer Fund, Director of SiliconBio and a lecturer at Anglia Ruskin University. He holds a Masters in Pharmacology (1st Class) from the University of Glasgow and pursued a PhD in functional genomics at the University of Cambridge. His research career in academia (CRUK) and industry (MedImmune) focused on understanding how tumours evolve and become resistant to treatment. He has published numerous scientific papers, book chapters and literature reviews, and has 300+ citations.

 

 

 

genomics artificial intelligence medicine healthcare AI machine learning

Harry Clifford:

Harry is Co-founder and Chief Technology Officer at Cambridge Cancer Genomics. He leads CCG’s tech team of AI researchers, data scientists and bioinformaticians to conduct innovative R&D and develop the underlying tumor analysis pipelines and cloud architecture of CCG’s smart genomics systems. Harry has a PhD in bioinformatics from Oxford University and experience in postdoctoral roles, including at Cambridge University with Cancer Research UK; and in the biopharma industry, where he worked on developing biomarker-based medical diagnostics.