Artificial Intelligence in Metabolism (AIM)

The conference brings together renowned investigators to discuss how cutting-edge data science can be best utilized to challenge current dogmas and catalyze better translation in metabolic and biomedical research.

Logo for the conference

Dates

June 2, June 16 & July 1, 2021


Background

Significant new discoveries in metabolic research increasingly rely on large-scale profiling, data integration and machine learning-based techniques.  Not only can these techniques be leveraged to distil novel actionable biological hypotheses to be tested in relevant physiological contexts/clinical settings, they for example enable us to develop predictive tools to more effectively and timely identify at-risk individuals or personalized drugs/interventions.

At the Novo Nordisk Foundation Center for Basic Metabolic Research (CMBR) we have set up enabling biology platforms that utilize sophisticated metabolomics, single-cell and data integration techniques to enrich our deeply phenotyped and genotyped patient cohorts.  These data along with the associated Danish longitudinal health register information constitute a unique base to empower current and next generations of scientists to tackle the major questions in metabolic research.

We envision that data and machine learning models will be at the heart of future teams driving novel discoveries in metabolic research, through interdisciplinary approaches involving traditional biomedical scientists alongside data scientists, mathematicians and bioinformaticians.  Please join us in our effort to discuss and incubate new ideas to better explore and model metabolic disease.


Program

Wednesday, June 2

16:00 - 16:45

'Protein interaction based analysis of GWAS linked genes for 1000 human traits'

 

 

16:45 - 17:30

'Understanding the spatiotemporal  subcellular organization of the human proteome'

 

 

Co-chairs:

Associate Professor Mani Arumugam

Postdoc Asker Daniel Brejnrod


Wednesday, June 16

16:00 - 16:45

'GWAS gene prioritization from local and polygenic signals'

 

 

16:45 - 17:30

'Reproducible machine learning in health data science'

 

 

Co-chairs:

Associate Professor Tune H Pers

Staff Scientist Christian Grønbæk


Thursday, July 1

16:00 - 16:45

'Machine learning for exploring biological systems'

 

 

16:45 - 17:30

'New single-cell technologies to dissect reprogramming and development'

 

 

Co-chairs:

Professor Kei Sakamoto

Assistant Professor Kristine Williams


Organising Committee