AI Lab for Molecular Medicine

AI Lab for Molecular Medicine

The UMC Utrecht AI lab for Molecular Medicine leverages AI to analyze molecular data, advancing precision medicine. Through collaboration, we develop powerful models to unlock the potential of vast cohorts and biobanks.

About uitklapper, klik om te openen

Molecular sciences are critical to achieving the promise of precision medicine. With advances in technology, we can measure genome-wide molecular profiles at an unprecedented level of detail. Disease-relevant tissues are routinely assayed using methods such as whole genome sequencing, transcriptomics, and methylome profiling, providing rich multi-modal datasets that elucidate the molecular mechanisms underlying disease and inform the development of novel diagnostics and therapies. 

In recent years, machine learning and artificial intelligence have made significant strides, fueled by the explosion of training data and computational power. These developments have enormous potential for molecular science, enabling researchers to tailor state-of-the-art AI algorithms to important biological questions. For instance, deep learning-based algorithm Alphafold2 solved one of the "big" questions in biology: how do proteins fold? This progress demonstrates the tremendous opportunities to apply AI to further advance our understanding of molecular mechanisms underlying disease. 

The UMC Utrecht AI lab for Molecular Medicine aims to harness the power of AI to analyze the vast molecular data available and develop new precision medicine approaches. The lab brings together molecular scientists, clinicians, and bioinformaticians to develop powerful AI models that can unlock the potential of the wealth of cohorts and biobanks available at the institution. Through collaboration and cutting-edge research, the lab aims to deliver the next generation of precision medicine. 

Coordinators uitklapper, klik om te openen

dr. Jeroen J. de Ridder J.deRidder-4@umcutrecht.nl

Prof. dr. Karijn K.P.M. Suijkerbuijk K.Suijkerbuijk@umcutrecht.nl

Myrthe Jager m.jager-@umcutrecht.nl

Projects uitklapper, klik om te openen

• Ultra-fast deep-learned pediatric CNS tumor classification during surgery
• Predicting the effect of non-coding mutations in cancer from DNA sequence alone
• Unravelling checkpoint inhibitor toxicity and efficacy (UNICIT)
• Predicting response to immune checkpoint inhibition using machine learning (PREMIUM)

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