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Foundation models for molecular diagnostics - machine learning with biological ‘common sense’

CORDIS · observation · Publication date unknown

Foundation models for molecular diagnostics - machine learning with biological ‘common sense’ Molecular diagnostics is crucial in fulfilling the promise of personalized medicine. While we are amidst an AI revolution, current machine learning models (ML) struggle to effectively learn from molecular (‘omics’) patient profiles and fail to make robust predictions. Perhaps this is not a surprise. After all, molecular disease biology is immensely complex, and we ask ML models to predict such complicated things as patient prognosis, without them ‘knowing’ anything about molecular biology and based on limited training data. To address this, I will create foundation models on top of the vast troves of available biomolecular data, such as multi-omics profiles in healthy and diseased tissues, high-resolution single-cell data and biological knowledge graphs. This unique approach is driven by self-supervised learning (SSL), an important driver

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recordType

award

status

SIGNED

region

EU

value

2000000

unit

EUR