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FoundationDX

Financiado

Foundation models for molecular diagnostics - machine learning with biological ‘...

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 profile... ver más

30/04/2029

UMC UTRECHT

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITAIR MEDISCH CENTRUM UTRECHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-02-27

ERC-2023-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

I+D

Cerrada hace 1 año

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1 Participantes

UMC UTRECHT

2.00M€ | Lider

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Últimas noticias

02-12-2024: AGENCIA-VALENCIANA-D... En las últimas 48 horas el Organismo AGENCIA VALENCIANA D... ha otorgado 1 concesiones

02-12-2024: CDTI En las últimas 48 horas el Organismo CDTI ha otorgado 4 concesiones

01-12-2024: REDES En las últimas 48 horas el Organismo REDES ha otorgado 1337 concesiones

Duración del proyecto: 62 meses Fecha Inicio: 2024-02-27
Fecha Fin: 2029-04-30

Líder del proyecto

UNIVERSITAIR MEDISCH CENTRUM UTRECHT

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto 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 of AI, which offers the opportunity to learn a comprehensive representation of the multimodal biology of the cell – without the need for well-annotated patient data. Starting from this strong basis, the FoundationDX model can then reliably predict cancer subtype or prognosis as it no longer needs to start from scratch on too high-dimensional, too low sample-size datasets. Effectively, we give our systems biological ‘common sense’, foregoing the need for millions of labeled training samples. This uniquely enables us to address one of the most clinically relevant questions: which treatment is best for the patient? The FoundationDX research program is designed to deliver key insights into how the SSL revolution can be used to drive progress in the field of molecular diagnostics. It contains a ‘clinical-grade’ benchmarking module and solves three urgent diagnostic challenges, including noninvasive subtyping of pediatric brain cancer. The time for powerful, robust and generalizable, knowledge-aware machine learning solutions to previously intractable molecular diagnostics problems has come. FoundationDX aims to deliver this.

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