Overview
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Title of the project to which the candidate would be incorporated:
Interpretable deep learning for inferring cellular transcriptional and metabolic changes of genetic perturbations in cancer (PERMET2CAN)
PID2 OB-I00 - Call: Knowledge Generation Projects 2024
Object of the position:
The main objective of the project is to develop a new framework based on interpretable Deep Learning (DL) to jointly infer transcriptional outcomes and metabolic changes associated with genetic perturbations. The design of the DL model will be guided by a priori biological information and will take into account two simultaneous objectives. Single-cell RNA sequencing (scRNA-Seq) data from perturbation experiments will be used. The proposed framework will allow identifying key cellular responses, transcriptional and metabolic, res...