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Precision cancer medicine with machine learning and AI

Academy Research Fellow Vittorio Fortino has been awarded a grant of 211 500 € by Jane and Aatos Erkko Foundation for the research project Combining multi-omics data and AI for disease subtype and drug discoveries. Fortino is Associate Professor of Health Bioinformatics at the Institute of Biomedicine.

Emerging technologies, big data, and increased pressure to contain health costs are creating a major opportunity for precision medicine. Precision medicine seeks to leverage advances in sequencing technologies and ‘omics fields to allow healthcare interventions to be tailored to patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. This approach is deeply different from traditional medical practice that is based on the idea that traditional treatments, which have been established based on a large series of patients, are considered universal solutions for treating new patients. However, cancer and other chronic diseases are known to be highly heterogeneous. They are often characterized by numerous subtypes, each with its own trajectory. Importantly, omics technologies such as genomics, proteomics and metabolomics can be utilized to characterize their molecular basis and to optimize preventive health care strategies. However, the use of omics technologies generates large volumes of data, and their utility depends mostly on the adopted algorithms and statistical methods. In particular, machine learning and artificial intelligence can unlock the potential of this wealth of data to improve our understanding of cancer biology and design personalized treatments.

The present project has an ambitious goal: to develop an AI-based system for classifying patients into subpopulations that differ in their susceptibility to a particular cancer type or subtype and for systematic pre-clinical screening of potential drug combinations. If successful, the project will enhance a new generation of more reliable decision-making tools for cancer precision medicine that can significantly improve outcomes for both clinicians and patients.

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