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Polygenic study and gentic intercations unravelled with AI for inherited diseases

keywords ARTIFICIAL INTELLIGENCE, GENETIC CODE, POLYGENIC

Reference persons VALENTINA ALICE CAUDA

External reference persons UniversitÓ degli Studi di Troino - Dip. di Genetica

Research Groups AA - Materials and Processes for Micro and Nano Technologies

Thesis type APPLIED RESEARCH, MODELING AND DATA ANALYSIS

Description Most heritable diseases depends on infromations encoded in genese and largely these gene variations are polygenic, i.e. due to multiple genes worign together. To comprehend the underlying genetic architecture, it is
crucial to discover the clinically relevant epistatic interactions between genomic single nucleotide polymorphisms . Existing statistical computational methods for thsi detection are mostly limited to pairs more than one gene due to the combinatorial explosion of higher-order interactions. Within this Master Thesis, the student will be inserted in a multidisciplinary context at Politecnico and with the Dept. of Genetic of Turin University to leverage network medicine and inform the selection of interaction that are an order of magnitude more statistically significant compared to existing tools. This method will be applied to Chronic Back Pain and discover genes that are partly known to affect the disease, and to proofs if these results are reproducible across independent cohorts of patients. Data base will be given by the thesi supervisor.

Required skills Quantum computation
Artificioal intelligence
Phyton
data selection and analyssi
database filtration


Deadline 22/02/2025      PROPONI LA TUA CANDIDATURA




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