Cancer is one of the leading causes of death worldwide and in Valais, imposing a profound burden on patients, families, and the healthcare system. Altered cell metabolism is a hallmark of cancer; thus, understanding how cancer cells reprogram their metabolism is crucial for precision oncology and the successful treatment of cancer patients. However, current molecular measurements alone cannot fully explain how tumors reprogram molecular pathways to grow, survive, or resist treatment. RNA profiles are already used in clinical settings, for example, to classify tumor subtypes or to guide therapeutic decisions, e.g., through gene expression signatures. These approaches treat RNA as a descriptive snapshot. Here, we propose to take a step further by using RNA to infer a complete picture of cellular metabolism, that is, the intricate network of chemical reactions that sustain cell’s growth and survival. Genome-scale metabolic models (GEMs) provide a static, mechanistic map of cellular metabolism that RNA profiles can constrain. However, most existing approaches simply overlay expression data onto a metabolic model for a single sample at a time; they do not learn how metabolism behaves across diverse cancer types and conditions. As a result, today’s methods cannot capture the underlying regulatory processes that shape metabolic activity.Our project introduces a fundamentally new approach. Inspired by how large language models learn patterns from vast text corpora, we propose a self-supervised learning framework that harnesses large collections of RNA-seq profiles to uncover the hidden relationships between gene expression and metabolism. The system learns directly from data, without requiring annotated examples, allowing it to capture regulatory behaviors that are not explicitly encoded in current models. Once trained, the framework can generalize across many cancer contexts and specialize to an individual patient, producing personalized metabolic profiles that may support treatment planning, subtype identification, and the discovery of metabolic vulnerabilities.The objective of this project is to transform RNA-seq data into a functional and predictive description of cancer cell metabolism, enabling the identification of metabolic vulnerabilities relevant to precision oncology. To achieve this, we aim to learn the hidden regulatory processes underlying the network of metabolic reactions. Specifically, the project pursues the following aims: (1) to systematically assemble and curate large-scale RNA-seq datasets relevant to cancer metabolism; (2) to develop a deep learning surrogate of genome-scale metabolic models that efficiently captures metabolic behaviour; (3) to design a cross-validation framework that optimizes coherence between transcriptomic data and metabolic predictions, forming the basis of a self-supervised learning strategy; (4) to extend the surrogate into a predictive model capable of learning hidden regulatory processes that control metabolic activity; and (5) to validate the complete framework on established cancer benchmarks, with the longer-term goal of enabling patient-specific metabolic characterization to support personalized cancer treatment.Our proposition represents a major innovation and has the potential to transform RNA-seq data (already widely available in oncology) into physiological insights, enabling scalable, automated, and clinically actionable characterization of cancer metabolism. Embedded within the strong and expanding health ecosystem of Valais and aligned with strategic priorities in AI at both the cantonal and institutional levels, the proposed project will also nucleate new, important partnerships between HES-SO, Idiap, and the hospital. Beyond its direct impact, it will also reinforce the canton’s position as a leader in AI and health innovation.