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events Jun 27, 2017

Special Interest Group AI in Biomedicine Meet-up

Join the second meet-up of the Special Interest Group on Artificial Intelligence in Biomedicine

Hosted by

Special Interest Group on AI in Biomedicine


Jun 27, 2017


04:00 PM


The Francis Crick Institute 1 Midland Rd, Kings Cross, London NW1 1AT

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The group has been developed by Knowledge Quarter partners including The Alan Turing Institute, BenevolentAI, The Francis Crick Institute, UCL and The Wellcome Trust.  The aim is to share learning, impart knowledge on the latest developments and advancements in AI and biomedicine, encourage networking and generate open collaboration and discussion between experts in AI, machine learning and biomedical scientists.

We encourage and welcome anyone who has an interest in AI and biomedicine,  whether you are an expert in your domain, professor, student, engineer, data scientist, biologist, academic or memebr of industry – all are welcome.


Schedule of the evening


4pm Registration


4.30pm Markus Ralser, Group Leader at the Francis Crick Institute

Title: The metabolome of a yeast cell predicted from its proteome

The central dogma of molecular biology predicts the hierarchical dependency of genome, transcriptome and proteome. Yet, one typically fails to explain even simple cellular phenotypes even if all three are known. A particular challenge represent metabolite concentrations.

We created highly precise proteomes and metabolomes for all viable Saccharomyces kinase knock-out strains, known to possesses strong metabolic differences without per se being growth deficient. We find that the 97 proteomes are largely dominated by differential enzyme expression, enabling to systematically link enzyme levels and metabolite concentrations using artificial intelligence. 

We established an AI workflow that combines metabolic network topology, multiple feature selection, and 13 machine learning algorithms we achieve the prediction of yeast cell metabolomes out of their proteome. We reveal a genome spanning regulatory network in which every kinase plays a specific role. A major mechanism of metabolic gene expression regulation operates through redistributing flux control to different enzymes, resulting in the complex interdependency of enzyme- and metabolite levels Demonstrating the predictability of the metabolome, we show that a new generation of data-driven biology can solve the genotype-phenotype problem.


About Markus Rasier

Markus graduated in Molecular Biology and Applied Informatics from the University of Sazburg, Austria and holds a PhD in Neurobiology from the Max Planck Institute for Molecular Genetics in Berlin where he was a Junior Group Leader from 2007 to 2011.  He then moved on to The University of Cambridge and Cambridge Systems Biology Centre where he has held the position of Group Leader since 2011. Since 2013, Markus has also been a Group Leader at The Francis Crick Institute (ex MRC-NIMR).


5pm – 5.30pm: Jane Reed, Head of Life Science Strategy, Linguamatics

Title: Bridging the gaps - NLP in translational research

For the past few years, artificial intelligence (AI) technologies such as natural language processing (NLP) have been hot topics in biomedicine, as researchers and healthcare providers consider ways to leverage technology to transform bioscience research and clinical care.

Accessing the right information is critical but much of the data is locked in textual format, such as scientific literature, clinical trial reports or electronic health records. NLP can effectively speed the extraction of critical information from unstructured scientific and clinical text. Use cases span discovery, development, and healthcare delivery – such as utilization of text mining for genotype-phenotype annotation, selecting patients for clinical trials, and extracting key endpoints from pathology reports and EHRs for better patient care.


About Jane Reed

Jane leads the strategic vision for Linguamatics’ growing product portfolio and business development in the life science domain. Jane has extensive experience in life sciences informatics. She worked for more than 15 years in vendor companies supplying data products, data integration and analysis and consultancy to pharma and biotech - with roles at Instem, BioWisdom, Incyte, and Hexagen. Before moving into the life science industry, Jane worked in academia with post-docs in genetics and genomics.


5.30pm – 7pm: Networking Drinks



If you wish to attend this meet-up, please contact the SIG coordinator at [email protected] 



If you would like to promote your company, its work or a specific project that involves integrating Artificial Intelligence in biomedicine, we would welcome you to produce a poster to display in the networking area.  To apply for the next meet-up, please send your abstract to [email protected]